Xphoto (OpenCV 4.1.2 Java documentation)

java.lang.Object
- org.opencv.xphoto.Xphoto

```
public class Xphoto
extends Object
```

Field Summary

Fields
Modifier and Type Field and Description

static int BM3D_STEP1

static int BM3D_STEP2

static int BM3D_STEPALL

static int HAAR

static int INPAINT_SHIFTMAP

Fields
Modifier and Type	Field and Description
`static int`	`BM3D_STEP1`
`static int`	`BM3D_STEP2`
`static int`	`BM3D_STEPALL`
`static int`	`HAAR`
`static int`	`INPAINT_SHIFTMAP`

Constructor Summary

Constructors
Constructor and Description

Xphoto()

Constructors
Constructor and Description
`Xphoto()`

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method and Description
`static void`	`applyChannelGains(Mat src, Mat dst, float gainB, float gainG, float gainR)` Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms.
`static void`	`bm3dDenoising(Mat src, Mat dst)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static void`	`bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)` Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations.
`static GrayworldWB`	`createGrayworldWB()` Creates an instance of GrayworldWB
`static LearningBasedWB`	`createLearningBasedWB()` Creates an instance of LearningBasedWB
`static LearningBasedWB`	`createLearningBasedWB(String path_to_model)` Creates an instance of LearningBasedWB
`static SimpleWB`	`createSimpleWB()` Creates an instance of SimpleWB
`static TonemapDurand`	`createTonemapDurand()` Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those.
`static TonemapDurand`	`createTonemapDurand(float gamma)` Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those.
`static TonemapDurand`	`createTonemapDurand(float gamma, float contrast)` Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those.
`static TonemapDurand`	`createTonemapDurand(float gamma, float contrast, float saturation)` Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those.
`static TonemapDurand`	`createTonemapDurand(float gamma, float contrast, float saturation, float sigma_space)` Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those.
`static TonemapDurand`	`createTonemapDurand(float gamma, float contrast, float saturation, float sigma_space, float sigma_color)` Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those.
`static void`	`dctDenoising(Mat src, Mat dst, double sigma)` The function implements simple dct-based denoising <http://www.ipol.im/pub/art/2011/ys-dct/>.
`static void`	`dctDenoising(Mat src, Mat dst, double sigma, int psize)` The function implements simple dct-based denoising <http://www.ipol.im/pub/art/2011/ys-dct/>.
`static void`	`inpaint(Mat src, Mat mask, Mat dst, int algorithmType)` The function implements different single-image inpainting algorithms.
`static void`	`oilPainting(Mat src, Mat dst, int size, int dynRatio)` oilPainting See the book CITE: Holzmann1988 for details.
`static void`	`oilPainting(Mat src, Mat dst, int size, int dynRatio, int code)` oilPainting See the book CITE: Holzmann1988 for details.

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

- Field Detail
  - INPAINT_SHIFTMAP
```
public static final int INPAINT_SHIFTMAP
```
    See Also:
    
    Constant Field Values
  - BM3D_STEPALL
```
public static final int BM3D_STEPALL
```
    See Also:
    
    Constant Field Values
  - BM3D_STEP1
```
public static final int BM3D_STEP1
```
    See Also:
    
    Constant Field Values
  - BM3D_STEP2
```
public static final int BM3D_STEP2
```
    See Also:
    
    Constant Field Values
  - HAAR
```
public static final int HAAR
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - Xphoto
```
public Xphoto()
```
- Method Detail
  - createGrayworldWB
```
public static GrayworldWB createGrayworldWB()
```
    Creates an instance of GrayworldWB
    
    Returns:
    
    automatically generated
  - createLearningBasedWB
```
public static LearningBasedWB createLearningBasedWB(String path_to_model)
```
    Creates an instance of LearningBasedWB
    
    Parameters:
    
    path_to_model - Path to a .yml file with the model. If not specified, the default model is used
    
    Returns:
    
    automatically generated
  - createLearningBasedWB
```
public static LearningBasedWB createLearningBasedWB()
```
    Creates an instance of LearningBasedWB
    
    Returns:
    
    automatically generated
  - createSimpleWB
```
public static SimpleWB createSimpleWB()
```
    Creates an instance of SimpleWB
    
    Returns:
    
    automatically generated
  - createTonemapDurand
```
public static TonemapDurand createTonemapDurand(float gamma,
                                                float contrast,
                                                float saturation,
                                                float sigma_space,
                                                float sigma_color)
```
    Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    contrast - resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
    
    saturation - saturation enhancement value. See createTonemapDrago
    
    sigma_space - bilateral filter sigma in color space
    
    sigma_color - bilateral filter sigma in coordinate space
    
    Returns:
    
    automatically generated
  - createTonemapDurand
```
public static TonemapDurand createTonemapDurand(float gamma,
                                                float contrast,
                                                float saturation,
                                                float sigma_space)
```
    Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    contrast - resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
    
    saturation - saturation enhancement value. See createTonemapDrago
    
    sigma_space - bilateral filter sigma in color space
    
    Returns:
    
    automatically generated
  - createTonemapDurand
```
public static TonemapDurand createTonemapDurand(float gamma,
                                                float contrast,
                                                float saturation)
```
    Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    contrast - resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
    
    saturation - saturation enhancement value. See createTonemapDrago
    
    Returns:
    
    automatically generated
  - createTonemapDurand
```
public static TonemapDurand createTonemapDurand(float gamma,
                                                float contrast)
```
    Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap
    
    contrast - resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
    
    Returns:
    
    automatically generated
  - createTonemapDurand
```
public static TonemapDurand createTonemapDurand(float gamma)
```
    Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
    
    Parameters:
    
    gamma - gamma value for gamma correction. See createTonemap are maximum and minimum luminance values of the resulting image.
    
    Returns:
    
    automatically generated
  - createTonemapDurand
```
public static TonemapDurand createTonemapDurand()
```
    Creates TonemapDurand object You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. are maximum and minimum luminance values of the resulting image.
    
    Returns:
    
    automatically generated
  - applyChannelGains
```
public static void applyChannelGains(Mat src,
                                     Mat dst,
                                     float gainB,
                                     float gainG,
                                     float gainR)
```
    Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms.
    
    Parameters:
    
    src - Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3)
    
    dst - Output image of the same size and type as src.
    
    gainB - gain for the B channel
    
    gainG - gain for the G channel
    
    gainR - gain for the R channel
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta,
                                 int normType,
                                 int step,
                                 int transformType)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
    
    normType - Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
    
    step - Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
    
    transformType - Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta,
                                 int normType,
                                 int step)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
    
    normType - Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
    
    step - Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta,
                                 int normType)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
    
    normType - Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h,
                                 int templateWindowSize)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst,
                                 float h)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src.
    
    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. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dst)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dst - Output image with the same size and type as src. removes image details, smaller h value preserves details but also preserves some noise. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta,
                                 int normType,
                                 int step,
                                 int transformType)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
    
    normType - Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
    
    step - Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
    
    transformType - Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta,
                                 int normType,
                                 int step)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
    
    normType - Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
    
    step - Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta,
                                 int normType)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
    
    normType - Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep,
                                 float beta)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block.
    
    beta - Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize,
                                 int slidingStep)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering.
    
    slidingStep - Sliding step to process every next reference block. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2,
                                 int groupSize)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    groupSize - Maximum size of the 3D group for collaborative filtering. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1,
                                 int blockMatchingStep2)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
    
    blockMatchingStep2 - Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize,
                                 int blockMatchingStep1)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
    
    blockMatchingStep1 - Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize,
                                 int searchWindowSize)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2.
    
    searchWindowSize - Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h,
                                 int templateWindowSize)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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.
    
    templateWindowSize - Size in pixels of the template patch that is used for block-matching. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2,
                                 float h)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src.
    
    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. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - bm3dDenoising
```
public static void bm3dDenoising(Mat src,
                                 Mat dstStep1,
                                 Mat dstStep2)
```
    Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
    
    Parameters:
    
    src - Input 8-bit or 16-bit 1-channel image.
    
    dstStep1 - Output image of the first step of BM3D with the same size and type as src.
    
    dstStep2 - Output image of the second step of BM3D with the same size and type as src. removes image details, smaller h value preserves details but also preserves some noise. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. Currently only Haar transform is supported. This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces. SEE: fastNlMeansDenoising
  - dctDenoising
```
public static void dctDenoising(Mat src,
                                Mat dst,
                                double sigma,
                                int psize)
```
    The function implements simple dct-based denoising <http://www.ipol.im/pub/art/2011/ys-dct/>.
    
    Parameters:
    
    src - source image
    
    dst - destination image
    
    sigma - expected noise standard deviation
    
    psize - size of block side where dct is computed SEE: fastNlMeansDenoising
  - dctDenoising
```
public static void dctDenoising(Mat src,
                                Mat dst,
                                double sigma)
```
    The function implements simple dct-based denoising <http://www.ipol.im/pub/art/2011/ys-dct/>.
    
    Parameters:
    
    src - source image
    
    dst - destination image
    
    sigma - expected noise standard deviation SEE: fastNlMeansDenoising
  - inpaint
```
public static void inpaint(Mat src,
                           Mat mask,
                           Mat dst,
                           int algorithmType)
```
    The function implements different single-image inpainting algorithms. See the original paper CITE: He2012 for details.
    
    Parameters:
    
    src - source image, it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces.
    
    mask - mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted
    
    dst - destination image
    
    algorithmType - see xphoto::InpaintTypes
  - oilPainting
```
public static void oilPainting(Mat src,
                               Mat dst,
                               int size,
                               int dynRatio,
                               int code)
```
    oilPainting See the book CITE: Holzmann1988 for details.
    
    Parameters:
    
    src - Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
    
    dst - Output image of the same size and type as src.
    
    size - neighbouring size is 2-size+1
    
    dynRatio - image is divided by dynRatio before histogram processing
    
    code - automatically generated
  - oilPainting
```
public static void oilPainting(Mat src,
                               Mat dst,
                               int size,
                               int dynRatio)
```
    oilPainting See the book CITE: Holzmann1988 for details.
    
    Parameters:
    
    src - Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
    
    dst - Output image of the same size and type as src.
    
    size - neighbouring size is 2-size+1
    
    dynRatio - image is divided by dynRatio before histogram processing

Class Xphoto

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

INPAINT_SHIFTMAP

BM3D_STEPALL

BM3D_STEP1

BM3D_STEP2

HAAR

Constructor Detail

Xphoto

Method Detail

createGrayworldWB

createLearningBasedWB

createLearningBasedWB

createSimpleWB

createTonemapDurand

createTonemapDurand

createTonemapDurand

createTonemapDurand

createTonemapDurand

createTonemapDurand

applyChannelGains

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

bm3dDenoising

dctDenoising

dctDenoising

inpaint

oilPainting

oilPainting