java.lang.Object
- org.opencv.photo.Photo

public class Photo
extends java.lang.Object

Field Summary

Fields
Modifier and Type	Field	Description
`static int`	`INPAINT_NS`
`static int`	`INPAINT_TELEA`
`static int`	`LDR_SIZE`
`static int`	`MIXED_CLONE`
`static int`	`MONOCHROME_TRANSFER`
`static int`	`NORMAL_CLONE`
`static int`	`NORMCONV_FILTER`
`static int`	`RECURS_FILTER`

Constructor Summary

Constructors
Constructor Description

Photo()

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method	Description
`static void`	`colorChange(Mat src, Mat mask, Mat dst)`	Given an original color image, two differently colored versions of this image can be mixed seamlessly.
`static void`	`colorChange(Mat src, Mat mask, Mat dst, float red_mul)`	Given an original color image, two differently colored versions of this image can be mixed seamlessly.
`static void`	`colorChange(Mat src, Mat mask, Mat dst, float red_mul, float green_mul)`	Given an original color image, two differently colored versions of this image can be mixed seamlessly.
`static void`	`colorChange(Mat src, Mat mask, Mat dst, float red_mul, float green_mul, float blue_mul)`	Given an original color image, two differently colored versions of this image can be mixed seamlessly.
`static AlignMTB`	`createAlignMTB()`	Creates AlignMTB object usually good enough (31 and 63 pixels shift respectively).
`static AlignMTB`	`createAlignMTB(int max_bits)`	Creates AlignMTB object
`static AlignMTB`	`createAlignMTB(int max_bits, int exclude_range)`	Creates AlignMTB object
`static AlignMTB`	`createAlignMTB(int max_bits, int exclude_range, boolean cut)`	Creates AlignMTB object
`static CalibrateDebevec`	`createCalibrateDebevec()`	Creates CalibrateDebevec object response.
`static CalibrateDebevec`	`createCalibrateDebevec(int samples)`	Creates CalibrateDebevec object
`static CalibrateDebevec`	`createCalibrateDebevec(int samples, float lambda)`	Creates CalibrateDebevec object
`static CalibrateDebevec`	`createCalibrateDebevec(int samples, float lambda, boolean random)`	Creates CalibrateDebevec object
`static CalibrateRobertson`	`createCalibrateRobertson()`	Creates CalibrateRobertson object
`static CalibrateRobertson`	`createCalibrateRobertson(int max_iter)`	Creates CalibrateRobertson object
`static CalibrateRobertson`	`createCalibrateRobertson(int max_iter, float threshold)`	Creates CalibrateRobertson object
`static MergeDebevec`	`createMergeDebevec()`	Creates MergeDebevec object
`static MergeMertens`	`createMergeMertens()`	Creates MergeMertens object
`static MergeMertens`	`createMergeMertens(float contrast_weight)`	Creates MergeMertens object
`static MergeMertens`	`createMergeMertens(float contrast_weight, float saturation_weight)`	Creates MergeMertens object
`static MergeMertens`	`createMergeMertens(float contrast_weight, float saturation_weight, float exposure_weight)`	Creates MergeMertens object
`static MergeRobertson`	`createMergeRobertson()`	Creates MergeRobertson object
`static Tonemap`	`createTonemap()`	Creates simple linear mapper with gamma correction equal to 2.2f is suitable for most displays.
`static Tonemap`	`createTonemap(float gamma)`	Creates simple linear mapper with gamma correction
`static TonemapDrago`	`createTonemapDrago()`	Creates TonemapDrago object than 1 increase saturation and values less than 1 decrease it.
`static TonemapDrago`	`createTonemapDrago(float gamma)`	Creates TonemapDrago object
`static TonemapDrago`	`createTonemapDrago(float gamma, float saturation)`	Creates TonemapDrago object
`static TonemapDrago`	`createTonemapDrago(float gamma, float saturation, float bias)`	Creates TonemapDrago object
`static TonemapMantiuk`	`createTonemapMantiuk()`	Creates TonemapMantiuk object dynamic range.
`static TonemapMantiuk`	`createTonemapMantiuk(float gamma)`	Creates TonemapMantiuk object
`static TonemapMantiuk`	`createTonemapMantiuk(float gamma, float scale)`	Creates TonemapMantiuk object
`static TonemapMantiuk`	`createTonemapMantiuk(float gamma, float scale, float saturation)`	Creates TonemapMantiuk object
`static TonemapReinhard`	`createTonemapReinhard()`	Creates TonemapReinhard object value, if 0 it's global, otherwise it's a weighted mean of this two cases.
`static TonemapReinhard`	`createTonemapReinhard(float gamma)`	Creates TonemapReinhard object
`static TonemapReinhard`	`createTonemapReinhard(float gamma, float intensity)`	Creates TonemapReinhard object
`static TonemapReinhard`	`createTonemapReinhard(float gamma, float intensity, float light_adapt)`	Creates TonemapReinhard object
`static TonemapReinhard`	`createTonemapReinhard(float gamma, float intensity, float light_adapt, float color_adapt)`	Creates TonemapReinhard object
`static void`	`decolor(Mat src, Mat grayscale, Mat color_boost)`	Transforms a color image to a grayscale image.
`static void`	`denoise_TVL1(java.util.List<Mat> observations, Mat result)`	Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional).
`static void`	`denoise_TVL1(java.util.List<Mat> observations, Mat result, double lambda)`	Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional).
`static void`	`denoise_TVL1(java.util.List<Mat> observations, Mat result, double lambda, int niters)`	Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional).
`static void`	`detailEnhance(Mat src, Mat dst)`	This filter enhances the details of a particular image.
`static void`	`detailEnhance(Mat src, Mat dst, float sigma_s)`	This filter enhances the details of a particular image.
`static void`	`detailEnhance(Mat src, Mat dst, float sigma_s, float sigma_r)`	This filter enhances the details of a particular image.
`static void`	`edgePreservingFilter(Mat src, Mat dst)`	Filtering is the fundamental operation in image and video processing.
`static void`	`edgePreservingFilter(Mat src, Mat dst, int flags)`	Filtering is the fundamental operation in image and video processing.
`static void`	`edgePreservingFilter(Mat src, Mat dst, int flags, float sigma_s)`	Filtering is the fundamental operation in image and video processing.
`static void`	`edgePreservingFilter(Mat src, Mat dst, int flags, float sigma_s, float sigma_r)`	Filtering is the fundamental operation in image and video processing.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, float h)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, float h, int templateWindowSize)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)`	Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations.
`static void`	`fastNlMeansDenoisingColored(Mat src, Mat dst)`	Modification of fastNlMeansDenoising function for colored images
`static void`	`fastNlMeansDenoisingColored(Mat src, Mat dst, float h)`	Modification of fastNlMeansDenoising function for colored images
`static void`	`fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor)`	Modification of fastNlMeansDenoising function for colored images
`static void`	`fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor, int templateWindowSize)`	Modification of fastNlMeansDenoising function for colored images
`static void`	`fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor, int templateWindowSize, int searchWindowSize)`	Modification of fastNlMeansDenoising function for colored images
`static void`	`fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize)`	Modification of fastNlMeansDenoisingMulti function for colored images sequences
`static void`	`fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h)`	Modification of fastNlMeansDenoisingMulti function for colored images sequences
`static void`	`fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor)`	Modification of fastNlMeansDenoisingMulti function for colored images sequences
`static void`	`fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize)`	Modification of fastNlMeansDenoisingMulti function for colored images sequences
`static void`	`fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize)`	Modification of fastNlMeansDenoisingMulti function for colored images sequences
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize, int searchWindowSize)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)`	Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time.
`static void`	`illuminationChange(Mat src, Mat mask, Mat dst)`	Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
`static void`	`illuminationChange(Mat src, Mat mask, Mat dst, float alpha)`	Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
`static void`	`illuminationChange(Mat src, Mat mask, Mat dst, float alpha, float beta)`	Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
`static void`	`inpaint(Mat src, Mat inpaintMask, Mat dst, double inpaintRadius, int flags)`	Restores the selected region in an image using the region neighborhood.
`static void`	`pencilSketch(Mat src, Mat dst1, Mat dst2)`	Pencil-like non-photorealistic line drawing
`static void`	`pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s)`	Pencil-like non-photorealistic line drawing
`static void`	`pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r)`	Pencil-like non-photorealistic line drawing
`static void`	`pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r, float shade_factor)`	Pencil-like non-photorealistic line drawing
`static void`	`seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat blend, int flags)`	Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection.
`static void`	`stylization(Mat src, Mat dst)`	Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism.
`static void`	`stylization(Mat src, Mat dst, float sigma_s)`	Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism.
`static void`	`stylization(Mat src, Mat dst, float sigma_s, float sigma_r)`	Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism.
`static void`	`textureFlattening(Mat src, Mat mask, Mat dst)`	By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect.
`static void`	`textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold)`	By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect.
`static void`	`textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold)`	By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect.
`static void`	`textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold, int kernel_size)`	By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - INPAINT_NS
```
public static final int INPAINT_NS
```
    See Also:
    
    Constant Field Values
  - INPAINT_TELEA
```
public static final int INPAINT_TELEA
```
    See Also:
    
    Constant Field Values
  - LDR_SIZE
```
public static final int LDR_SIZE
```
    See Also:
    
    Constant Field Values
  - NORMAL_CLONE
```
public static final int NORMAL_CLONE
```
    See Also:
    
    Constant Field Values
  - MIXED_CLONE
```
public static final int MIXED_CLONE
```
    See Also:
    
    Constant Field Values
  - MONOCHROME_TRANSFER
```
public static final int MONOCHROME_TRANSFER
```
    See Also:
    
    Constant Field Values
  - RECURS_FILTER
```
public static final int RECURS_FILTER
```
    See Also:
    
    Constant Field Values
  - NORMCONV_FILTER
```
public static final int NORMCONV_FILTER
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - Photo
```
public Photo()
```
- Method Detail
  - inpaint
```
public static void inpaint(Mat src,
                           Mat inpaintMask,
                           Mat dst,
                           double inpaintRadius,
                           int flags)
```
    Restores the selected region in an image using the region neighborhood.
    Parameters:
    
    src - Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
    
    inpaintMask - Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that needs to be inpainted.
    
    dst - Output image with the same size and type as src .
    
    inpaintRadius - Radius of a circular neighborhood of each point inpainted that is considered by the algorithm.
    
    flags - Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA The function reconstructs the selected image area from the pixel near the area boundary. The function may be used to remove dust and scratches from a scanned photo, or to remove undesirable objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details. Note:
    
    An example using the inpainting technique can be found at opencv_source_code/samples/cpp/inpaint.cpp
    
    (Python) An example using the inpainting technique can be found at opencv_source_code/samples/python/inpaint.py
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        float h,
                                        int templateWindowSize,
                                        int searchWindowSize)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        float h,
                                        int templateWindowSize)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        float h)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src . Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src . Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        MatOfFloat h,
                                        int templateWindowSize,
                                        int searchWindowSize,
                                        int normType)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
    
    normType - Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        MatOfFloat h,
                                        int templateWindowSize,
                                        int searchWindowSize)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        MatOfFloat h,
                                        int templateWindowSize)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoising
```
public static void fastNlMeansDenoising(Mat src,
                                        Mat dst,
                                        MatOfFloat h)
```
    Perform image denoising using Non-local Means Denoising algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational optimizations. Noise expected to be a gaussian white noise
    
    Parameters:
    
    src - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel image.
    
    dst - Output image with the same size and type as src . Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
  - fastNlMeansDenoisingColored
```
public static void fastNlMeansDenoisingColored(Mat src,
                                               Mat dst,
                                               float h,
                                               float hColor,
                                               int templateWindowSize,
                                               int searchWindowSize)
```
    Modification of fastNlMeansDenoising function for colored images
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
    
    hColor - The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.
  - fastNlMeansDenoisingColored
```
public static void fastNlMeansDenoisingColored(Mat src,
                                               Mat dst,
                                               float h,
                                               float hColor,
                                               int templateWindowSize)
```
    Modification of fastNlMeansDenoising function for colored images
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src .
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
    
    hColor - The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.
  - fastNlMeansDenoisingColored
```
public static void fastNlMeansDenoisingColored(Mat src,
                                               Mat dst,
                                               float h,
                                               float hColor)
```
    Modification of fastNlMeansDenoising function for colored images
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src . Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
    
    hColor - The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.
  - fastNlMeansDenoisingColored
```
public static void fastNlMeansDenoisingColored(Mat src,
                                               Mat dst,
                                               float h)
```
    Modification of fastNlMeansDenoising function for colored images
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src . Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise will be enough to remove colored noise and do not distort colors The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.
  - fastNlMeansDenoisingColored
```
public static void fastNlMeansDenoisingColored(Mat src,
                                               Mat dst)
```
    Modification of fastNlMeansDenoising function for colored images
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src . Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels removes noise but also removes image details, smaller h value preserves details but also preserves some noise will be enough to remove colored noise and do not distort colors The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             float h,
                                             int templateWindowSize,
                                             int searchWindowSize)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             float h,
                                             int templateWindowSize)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             float h)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             MatOfFloat h,
                                             int templateWindowSize,
                                             int searchWindowSize,
                                             int normType)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
    
    normType - Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             MatOfFloat h,
                                             int templateWindowSize,
                                             int searchWindowSize)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             MatOfFloat h,
                                             int templateWindowSize)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingMulti
```
public static void fastNlMeansDenoisingMulti(java.util.List<Mat> srcImgs,
                                             Mat dst,
                                             int imgToDenoiseIndex,
                                             int temporalWindowSize,
                                             MatOfFloat h)
```
    Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
    
    Parameters:
    
    srcImgs - Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  - fastNlMeansDenoisingColoredMulti
```
public static void fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs,
                                                    Mat dst,
                                                    int imgToDenoiseIndex,
                                                    int temporalWindowSize,
                                                    float h,
                                                    float hColor,
                                                    int templateWindowSize,
                                                    int searchWindowSize)
```
    Modification of fastNlMeansDenoisingMulti function for colored images sequences
    
    Parameters:
    
    srcImgs - Input 8-bit 3-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
    
    searchWindowSize - Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
    
    hColor - The same as h but for color components. The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.
  - fastNlMeansDenoisingColoredMulti
```
public static void fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs,
                                                    Mat dst,
                                                    int imgToDenoiseIndex,
                                                    int temporalWindowSize,
                                                    float h,
                                                    float hColor,
                                                    int templateWindowSize)
```
    Modification of fastNlMeansDenoisingMulti function for colored images sequences
    
    Parameters:
    
    srcImgs - Input 8-bit 3-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images.
    
    templateWindowSize - Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
    
    hColor - The same as h but for color components. The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.
  - fastNlMeansDenoisingColoredMulti
```
public static void fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs,
                                                    Mat dst,
                                                    int imgToDenoiseIndex,
                                                    int temporalWindowSize,
                                                    float h,
                                                    float hColor)
```
    Modification of fastNlMeansDenoisingMulti function for colored images sequences
    
    Parameters:
    
    srcImgs - Input 8-bit 3-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
    
    hColor - The same as h but for color components. The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.
  - fastNlMeansDenoisingColoredMulti
```
public static void fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs,
                                                    Mat dst,
                                                    int imgToDenoiseIndex,
                                                    int temporalWindowSize,
                                                    float h)
```
    Modification of fastNlMeansDenoisingMulti function for colored images sequences
    
    Parameters:
    
    srcImgs - Input 8-bit 3-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
    
    h - Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.
  - fastNlMeansDenoisingColoredMulti
```
public static void fastNlMeansDenoisingColoredMulti(java.util.List<Mat> srcImgs,
                                                    Mat dst,
                                                    int imgToDenoiseIndex,
                                                    int temporalWindowSize)
```
    Modification of fastNlMeansDenoisingMulti function for colored images sequences
    
    Parameters:
    
    srcImgs - Input 8-bit 3-channel images sequence. All images should have the same type and size.
    
    imgToDenoiseIndex - Target image to denoise index in srcImgs sequence
    
    temporalWindowSize - Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex + temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
    
    dst - Output image with the same size and type as srcImgs images. Should be odd. Recommended value 7 pixels given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels removes noise but also removes image details, smaller h value preserves details but also preserves some noise. The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.
  - denoise_TVL1
```
public static void denoise_TVL1(java.util.List<Mat> observations,
                                Mat result,
                                double lambda,
                                int niters)
```
    Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primal-dual algorithm then can be used to perform denoising and this is exactly what is implemented. It should be noted, that this implementation was taken from the July 2013 blog entry CITE: MA13 , which also contained (slightly more general) ready-to-use source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors. Although the thorough discussion and justification of the algorithm involved may be found in CITE: ChambolleEtAl, it might make sense to skim over it here, following CITE: MA13 . To begin with, we consider the 1-byte gray-level images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula \(\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\) \(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
    
    Parameters:
    
    observations - This array should contain one or more noised versions of the image that is to be restored.
    
    result - Here the denoised image will be stored. There is no need to do pre-allocation of storage space, as it will be automatically allocated, if necessary.
    
    lambda - Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed.
    
    niters - Number of iterations that the algorithm will run. Of course, as more iterations as better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor.
  - denoise_TVL1
```
public static void denoise_TVL1(java.util.List<Mat> observations,
                                Mat result,
                                double lambda)
```
    Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primal-dual algorithm then can be used to perform denoising and this is exactly what is implemented. It should be noted, that this implementation was taken from the July 2013 blog entry CITE: MA13 , which also contained (slightly more general) ready-to-use source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors. Although the thorough discussion and justification of the algorithm involved may be found in CITE: ChambolleEtAl, it might make sense to skim over it here, following CITE: MA13 . To begin with, we consider the 1-byte gray-level images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula \(\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\) \(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
    
    Parameters:
    
    observations - This array should contain one or more noised versions of the image that is to be restored.
    
    result - Here the denoised image will be stored. There is no need to do pre-allocation of storage space, as it will be automatically allocated, if necessary.
    
    lambda - Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed. better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor.
  - denoise_TVL1
```
public static void denoise_TVL1(java.util.List<Mat> observations,
                                Mat result)
```
    Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primal-dual algorithm then can be used to perform denoising and this is exactly what is implemented. It should be noted, that this implementation was taken from the July 2013 blog entry CITE: MA13 , which also contained (slightly more general) ready-to-use source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors. Although the thorough discussion and justification of the algorithm involved may be found in CITE: ChambolleEtAl, it might make sense to skim over it here, following CITE: MA13 . To begin with, we consider the 1-byte gray-level images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula \(\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\) \(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
    
    Parameters:
    
    observations - This array should contain one or more noised versions of the image that is to be restored.
    
    result - Here the denoised image will be stored. There is no need to do pre-allocation of storage space, as it will be automatically allocated, if necessary. (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed. better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor.
  - createTonemap
```
public static Tonemap createTonemap(float gamma)
```
    Creates simple linear mapper with gamma correction
    
    Parameters:
    
    gamma - positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma equal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it.
    
    Returns:
    
    automatically generated
  - createTonemap
```
public static Tonemap createTonemap()
```
    Creates simple linear mapper with gamma correction equal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it.
    
    Returns:
    
    automatically generated
  - createTonemapDrago
```
public static TonemapDrago createTonemapDrago(float gamma,
                                              float saturation,
                                              float bias)
```
    Creates TonemapDrago object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    saturation - positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it.
    
    bias - value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85.
    
    Returns:
    
    automatically generated
  - createTonemapDrago
```
public static TonemapDrago createTonemapDrago(float gamma,
                                              float saturation)
```
    Creates TonemapDrago object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    saturation - positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it. results, default value is 0.85.
    
    Returns:
    
    automatically generated
  - createTonemapDrago
```
public static TonemapDrago createTonemapDrago(float gamma)
```
    Creates TonemapDrago object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap than 1 increase saturation and values less than 1 decrease it. results, default value is 0.85.
    
    Returns:
    
    automatically generated
  - createTonemapDrago
```
public static TonemapDrago createTonemapDrago()
```
    Creates TonemapDrago object than 1 increase saturation and values less than 1 decrease it. results, default value is 0.85.
    
    Returns:
    
    automatically generated
  - createTonemapReinhard
```
public static TonemapReinhard createTonemapReinhard(float gamma,
                                                    float intensity,
                                                    float light_adapt,
                                                    float color_adapt)
```
    Creates TonemapReinhard object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    intensity - result intensity in [-8, 8] range. Greater intensity produces brighter results.
    
    light_adapt - light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases.
    
    color_adapt - chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel.
    
    Returns:
    
    automatically generated
  - createTonemapReinhard
```
public static TonemapReinhard createTonemapReinhard(float gamma,
                                                    float intensity,
                                                    float light_adapt)
```
    Creates TonemapReinhard object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    intensity - result intensity in [-8, 8] range. Greater intensity produces brighter results.
    
    light_adapt - light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases. if 0 adaptation level is the same for each channel.
    
    Returns:
    
    automatically generated
  - createTonemapReinhard
```
public static TonemapReinhard createTonemapReinhard(float gamma,
                                                    float intensity)
```
    Creates TonemapReinhard object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    intensity - result intensity in [-8, 8] range. Greater intensity produces brighter results. value, if 0 it's global, otherwise it's a weighted mean of this two cases. if 0 adaptation level is the same for each channel.
    
    Returns:
    
    automatically generated
  - createTonemapReinhard
```
public static TonemapReinhard createTonemapReinhard(float gamma)
```
    Creates TonemapReinhard object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap value, if 0 it's global, otherwise it's a weighted mean of this two cases. if 0 adaptation level is the same for each channel.
    
    Returns:
    
    automatically generated
  - createTonemapReinhard
```
public static TonemapReinhard createTonemapReinhard()
```
    Creates TonemapReinhard object value, if 0 it's global, otherwise it's a weighted mean of this two cases. if 0 adaptation level is the same for each channel.
    
    Returns:
    
    automatically generated
  - createTonemapMantiuk
```
public static TonemapMantiuk createTonemapMantiuk(float gamma,
                                                  float scale,
                                                  float saturation)
```
    Creates TonemapMantiuk object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    scale - contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results.
    
    saturation - saturation enhancement value. See createTonemapDrago
    
    Returns:
    
    automatically generated
  - createTonemapMantiuk
```
public static TonemapMantiuk createTonemapMantiuk(float gamma,
                                                  float scale)
```
    Creates TonemapMantiuk object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    scale - contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results.
    
    Returns:
    
    automatically generated
  - createTonemapMantiuk
```
public static TonemapMantiuk createTonemapMantiuk(float gamma)
```
    Creates TonemapMantiuk object
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap dynamic range. Values from 0.6 to 0.9 produce best results.
    
    Returns:
    
    automatically generated
  - createTonemapMantiuk
```
public static TonemapMantiuk createTonemapMantiuk()
```
    Creates TonemapMantiuk object dynamic range. Values from 0.6 to 0.9 produce best results.
    
    Returns:
    
    automatically generated
  - createAlignMTB
```
public static AlignMTB createAlignMTB(int max_bits,
                                      int exclude_range,
                                      boolean cut)
```
    Creates AlignMTB object
    
    Parameters:
    
    max_bits - logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively).
    
    exclude_range - range for exclusion bitmap that is constructed to suppress noise around the median value.
    
    cut - if true cuts images, otherwise fills the new regions with zeros.
    
    Returns:
    
    automatically generated
  - createAlignMTB
```
public static AlignMTB createAlignMTB(int max_bits,
                                      int exclude_range)
```
    Creates AlignMTB object
    
    Parameters:
    
    max_bits - logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively).
    
    exclude_range - range for exclusion bitmap that is constructed to suppress noise around the median value.
    
    Returns:
    
    automatically generated
  - createAlignMTB
```
public static AlignMTB createAlignMTB(int max_bits)
```
    Creates AlignMTB object
    
    Parameters:
    
    max_bits - logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively). median value.
    
    Returns:
    
    automatically generated
  - createAlignMTB
```
public static AlignMTB createAlignMTB()
```
    Creates AlignMTB object usually good enough (31 and 63 pixels shift respectively). median value.
    
    Returns:
    
    automatically generated
  - createCalibrateDebevec
```
public static CalibrateDebevec createCalibrateDebevec(int samples,
                                                      float lambda,
                                                      boolean random)
```
    Creates CalibrateDebevec object
    
    Parameters:
    
    samples - number of pixel locations to use
    
    lambda - smoothness term weight. Greater values produce smoother results, but can alter the response.
    
    random - if true sample pixel locations are chosen at random, otherwise they form a rectangular grid.
    
    Returns:
    
    automatically generated
  - createCalibrateDebevec
```
public static CalibrateDebevec createCalibrateDebevec(int samples,
                                                      float lambda)
```
    Creates CalibrateDebevec object
    
    Parameters:
    
    samples - number of pixel locations to use
    
    lambda - smoothness term weight. Greater values produce smoother results, but can alter the response. rectangular grid.
    
    Returns:
    
    automatically generated
  - createCalibrateDebevec
```
public static CalibrateDebevec createCalibrateDebevec(int samples)
```
    Creates CalibrateDebevec object
    
    Parameters:
    
    samples - number of pixel locations to use response. rectangular grid.
    
    Returns:
    
    automatically generated
  - createCalibrateDebevec
```
public static CalibrateDebevec createCalibrateDebevec()
```
    Creates CalibrateDebevec object response. rectangular grid.
    
    Returns:
    
    automatically generated
  - createCalibrateRobertson
```
public static CalibrateRobertson createCalibrateRobertson(int max_iter,
                                                          float threshold)
```
    Creates CalibrateRobertson object
    
    Parameters:
    
    max_iter - maximal number of Gauss-Seidel solver iterations.
    
    threshold - target difference between results of two successive steps of the minimization.
    
    Returns:
    
    automatically generated
  - createCalibrateRobertson
```
public static CalibrateRobertson createCalibrateRobertson(int max_iter)
```
    Creates CalibrateRobertson object
    
    Parameters:
    
    max_iter - maximal number of Gauss-Seidel solver iterations.
    
    Returns:
    
    automatically generated
  - createCalibrateRobertson
```
public static CalibrateRobertson createCalibrateRobertson()
```
    Creates CalibrateRobertson object
    
    Returns:
    
    automatically generated
  - createMergeDebevec
```
public static MergeDebevec createMergeDebevec()
```
    Creates MergeDebevec object
    
    Returns:
    
    automatically generated
  - createMergeMertens
```
public static MergeMertens createMergeMertens(float contrast_weight,
                                              float saturation_weight,
                                              float exposure_weight)
```
    Creates MergeMertens object
    
    Parameters:
    
    contrast_weight - contrast measure weight. See MergeMertens.
    
    saturation_weight - saturation measure weight
    
    exposure_weight - well-exposedness measure weight
    
    Returns:
    
    automatically generated
  - createMergeMertens
```
public static MergeMertens createMergeMertens(float contrast_weight,
                                              float saturation_weight)
```
    Creates MergeMertens object
    
    Parameters:
    
    contrast_weight - contrast measure weight. See MergeMertens.
    
    saturation_weight - saturation measure weight
    
    Returns:
    
    automatically generated
  - createMergeMertens
```
public static MergeMertens createMergeMertens(float contrast_weight)
```
    Creates MergeMertens object
    
    Parameters:
    
    contrast_weight - contrast measure weight. See MergeMertens.
    
    Returns:
    
    automatically generated
  - createMergeMertens
```
public static MergeMertens createMergeMertens()
```
    Creates MergeMertens object
    
    Returns:
    
    automatically generated
  - createMergeRobertson
```
public static MergeRobertson createMergeRobertson()
```
    Creates MergeRobertson object
    
    Returns:
    
    automatically generated
  - decolor
```
public static void decolor(Mat src,
                           Mat grayscale,
                           Mat color_boost)
```
    Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized black-and-white photograph rendering, and in many single channel image processing applications CITE: CL12 .
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    grayscale - Output 8-bit 1-channel image.
    
    color_boost - Output 8-bit 3-channel image. This function is to be applied on color images.
  - seamlessClone
```
public static void seamlessClone(Mat src,
                                 Mat dst,
                                 Mat mask,
                                 Point p,
                                 Mat blend,
                                 int flags)
```
    Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content CITE: PM03 .
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    p - Point in dst image where object is placed.
    
    blend - Output image with the same size and type as dst.
    
    flags - Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER
  - colorChange
```
public static void colorChange(Mat src,
                               Mat mask,
                               Mat dst,
                               float red_mul,
                               float green_mul,
                               float blue_mul)
```
    Given an original color image, two differently colored versions of this image can be mixed seamlessly.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src .
    
    red_mul - R-channel multiply factor.
    
    green_mul - G-channel multiply factor.
    
    blue_mul - B-channel multiply factor. Multiplication factor is between .5 to 2.5.
  - colorChange
```
public static void colorChange(Mat src,
                               Mat mask,
                               Mat dst,
                               float red_mul,
                               float green_mul)
```
    Given an original color image, two differently colored versions of this image can be mixed seamlessly.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src .
    
    red_mul - R-channel multiply factor.
    
    green_mul - G-channel multiply factor. Multiplication factor is between .5 to 2.5.
  - colorChange
```
public static void colorChange(Mat src,
                               Mat mask,
                               Mat dst,
                               float red_mul)
```
    Given an original color image, two differently colored versions of this image can be mixed seamlessly.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src .
    
    red_mul - R-channel multiply factor. Multiplication factor is between .5 to 2.5.
  - colorChange
```
public static void colorChange(Mat src,
                               Mat mask,
                               Mat dst)
```
    Given an original color image, two differently colored versions of this image can be mixed seamlessly.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src . Multiplication factor is between .5 to 2.5.
  - illuminationChange
```
public static void illuminationChange(Mat src,
                                      Mat mask,
                                      Mat dst,
                                      float alpha,
                                      float beta)
```
    Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    alpha - Value ranges between 0-2.
    
    beta - Value ranges between 0-2. This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
  - illuminationChange
```
public static void illuminationChange(Mat src,
                                      Mat mask,
                                      Mat dst,
                                      float alpha)
```
    Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    alpha - Value ranges between 0-2. This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
  - illuminationChange
```
public static void illuminationChange(Mat src,
                                      Mat mask,
                                      Mat dst)
```
    Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src. This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
  - textureFlattening
```
public static void textureFlattening(Mat src,
                                     Mat mask,
                                     Mat dst,
                                     float low_threshold,
                                     float high_threshold,
                                     int kernel_size)
```
    By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    low_threshold - %Range from 0 to 100.
    
    high_threshold - Value > 100.
    
    kernel_size - The size of the Sobel kernel to be used. Note: The algorithm assumes that the color of the source image is close to that of the destination. This assumption means that when the colors don't match, the source image color gets tinted toward the color of the destination image.
  - textureFlattening
```
public static void textureFlattening(Mat src,
                                     Mat mask,
                                     Mat dst,
                                     float low_threshold,
                                     float high_threshold)
```
    By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    low_threshold - %Range from 0 to 100.
    
    high_threshold - Value > 100. Note: The algorithm assumes that the color of the source image is close to that of the destination. This assumption means that when the colors don't match, the source image color gets tinted toward the color of the destination image.
  - textureFlattening
```
public static void textureFlattening(Mat src,
                                     Mat mask,
                                     Mat dst,
                                     float low_threshold)
```
    By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    low_threshold - %Range from 0 to 100. Note: The algorithm assumes that the color of the source image is close to that of the destination. This assumption means that when the colors don't match, the source image color gets tinted toward the color of the destination image.
  - textureFlattening
```
public static void textureFlattening(Mat src,
                                     Mat mask,
                                     Mat dst)
```
    By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    mask - Input 8-bit 1 or 3-channel image.
    
    dst - Output image with the same size and type as src. Note: The algorithm assumes that the color of the source image is close to that of the destination. This assumption means that when the colors don't match, the source image color gets tinted toward the color of the destination image.
  - edgePreservingFilter
```
public static void edgePreservingFilter(Mat src,
                                        Mat dst,
                                        int flags,
                                        float sigma_s,
                                        float sigma_r)
```
    Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications CITE: EM11 .
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output 8-bit 3-channel image.
    
    flags - Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
    
    sigma_s - %Range between 0 to 200.
    
    sigma_r - %Range between 0 to 1.
  - edgePreservingFilter
```
public static void edgePreservingFilter(Mat src,
                                        Mat dst,
                                        int flags,
                                        float sigma_s)
```
    Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications CITE: EM11 .
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output 8-bit 3-channel image.
    
    flags - Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
    
    sigma_s - %Range between 0 to 200.
  - edgePreservingFilter
```
public static void edgePreservingFilter(Mat src,
                                        Mat dst,
                                        int flags)
```
    Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications CITE: EM11 .
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output 8-bit 3-channel image.
    
    flags - Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
  - edgePreservingFilter
```
public static void edgePreservingFilter(Mat src,
                                        Mat dst)
```
    Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications CITE: EM11 .
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output 8-bit 3-channel image.
  - detailEnhance
```
public static void detailEnhance(Mat src,
                                 Mat dst,
                                 float sigma_s,
                                 float sigma_r)
```
    This filter enhances the details of a particular image.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
    
    sigma_r - %Range between 0 to 1.
  - detailEnhance
```
public static void detailEnhance(Mat src,
                                 Mat dst,
                                 float sigma_s)
```
    This filter enhances the details of a particular image.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
  - detailEnhance
```
public static void detailEnhance(Mat src,
                                 Mat dst)
```
    This filter enhances the details of a particular image.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src.
  - pencilSketch
```
public static void pencilSketch(Mat src,
                                Mat dst1,
                                Mat dst2,
                                float sigma_s,
                                float sigma_r,
                                float shade_factor)
```
    Pencil-like non-photorealistic line drawing
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst1 - Output 8-bit 1-channel image.
    
    dst2 - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
    
    sigma_r - %Range between 0 to 1.
    
    shade_factor - %Range between 0 to 0.1.
  - pencilSketch
```
public static void pencilSketch(Mat src,
                                Mat dst1,
                                Mat dst2,
                                float sigma_s,
                                float sigma_r)
```
    Pencil-like non-photorealistic line drawing
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst1 - Output 8-bit 1-channel image.
    
    dst2 - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
    
    sigma_r - %Range between 0 to 1.
  - pencilSketch
```
public static void pencilSketch(Mat src,
                                Mat dst1,
                                Mat dst2,
                                float sigma_s)
```
    Pencil-like non-photorealistic line drawing
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst1 - Output 8-bit 1-channel image.
    
    dst2 - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
  - pencilSketch
```
public static void pencilSketch(Mat src,
                                Mat dst1,
                                Mat dst2)
```
    Pencil-like non-photorealistic line drawing
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst1 - Output 8-bit 1-channel image.
    
    dst2 - Output image with the same size and type as src.
  - stylization
```
public static void stylization(Mat src,
                               Mat dst,
                               float sigma_s,
                               float sigma_r)
```
    Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
    
    sigma_r - %Range between 0 to 1.
  - stylization
```
public static void stylization(Mat src,
                               Mat dst,
                               float sigma_s)
```
    Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src.
    
    sigma_s - %Range between 0 to 200.
  - stylization
```
public static void stylization(Mat src,
                               Mat dst)
```
    Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.
    
    Parameters:
    
    src - Input 8-bit 3-channel image.
    
    dst - Output image with the same size and type as src.

Class Photo

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

INPAINT_NS

INPAINT_TELEA

LDR_SIZE

NORMAL_CLONE

MIXED_CLONE

MONOCHROME_TRANSFER

RECURS_FILTER

NORMCONV_FILTER

Constructor Detail

Photo

Method Detail

inpaint

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoising

fastNlMeansDenoisingColored

fastNlMeansDenoisingColored

fastNlMeansDenoisingColored

fastNlMeansDenoisingColored

fastNlMeansDenoisingColored

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingMulti

fastNlMeansDenoisingColoredMulti

fastNlMeansDenoisingColoredMulti

fastNlMeansDenoisingColoredMulti

fastNlMeansDenoisingColoredMulti

fastNlMeansDenoisingColoredMulti

denoise_TVL1

denoise_TVL1

denoise_TVL1

createTonemap

createTonemap

createTonemapDrago

createTonemapDrago

createTonemapDrago

createTonemapDrago

createTonemapReinhard

createTonemapReinhard

createTonemapReinhard

createTonemapReinhard

createTonemapReinhard

createTonemapMantiuk

createTonemapMantiuk

createTonemapMantiuk

createTonemapMantiuk

createAlignMTB

createAlignMTB

createAlignMTB

createAlignMTB

createCalibrateDebevec

createCalibrateDebevec

createCalibrateDebevec

createCalibrateDebevec

createCalibrateRobertson

createCalibrateRobertson

createCalibrateRobertson

createMergeDebevec

createMergeMertens

createMergeMertens

createMergeMertens

createMergeMertens

createMergeRobertson

decolor