Package org.opencv.xphoto
Class Xphoto
 java.lang.Object

 org.opencv.xphoto.Xphoto

public class Xphoto extends java.lang.Object


Field Summary
Fields Modifier and Type Field Description static int
BM3D_STEP1
static int
BM3D_STEP2
static int
BM3D_STEPALL
static int
HAAR
static int
INPAINT_FSR_BEST
static int
INPAINT_FSR_FAST
static int
INPAINT_SHIFTMAP

Constructor Summary
Constructors Constructor Description Xphoto()

Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method Description static void
applyChannelGains(Mat src, Mat dst, float gainB, float gainG, float gainR)
Implements an efficient fixedpoint 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations.static void
bm3dDenoising(Mat src, Mat dst, float h)
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations.static void
bm3dDenoising(Mat src, Mat dst, float h, int templateWindowSize)
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations.static void
bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2)
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations.static void
bm3dDenoising(Mat src, Mat dstStep1, Mat dstStep2, float h)
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations.static GrayworldWB
createGrayworldWB()
Creates an instance of GrayworldWBstatic LearningBasedWB
createLearningBasedWB()
Creates an instance of LearningBasedWBstatic LearningBasedWB
createLearningBasedWB(java.lang.String path_to_model)
Creates an instance of LearningBasedWBstatic SimpleWB
createSimpleWB()
Creates an instance of SimpleWBstatic 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_color)
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_color, float sigma_space)
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 dctbased denoising <http://www.ipol.im/pub/art/2011/ysdct/>.static void
dctDenoising(Mat src, Mat dst, double sigma, int psize)
The function implements simple dctbased denoising <http://www.ipol.im/pub/art/2011/ysdct/>.static void
inpaint(Mat src, Mat mask, Mat dst, int algorithmType)
The function implements different singleimage 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.



Field Detail

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

INPAINT_SHIFTMAP
public static final int INPAINT_SHIFTMAP
 See Also:
 Constant Field Values

INPAINT_FSR_BEST
public static final int INPAINT_FSR_BEST
 See Also:
 Constant Field Values

INPAINT_FSR_FAST
public static final int INPAINT_FSR_FAST
 See Also:
 Constant Field Values

HAAR
public static final int HAAR
 See Also:
 Constant Field Values


Method Detail

inpaint
public static void inpaint(Mat src, Mat mask, Mat dst, int algorithmType)
The function implements different singleimage inpainting algorithms. See the original papers CITE: He2012 (Shiftmap) or CITE: GenserPCS2018 and CITE: SeilerTIP2015 (FSR) for details. Parameters:
src
 source image #INPAINT_SHIFTMAP: it could be of any type and any number of channels from 1 to 4. In case of 3 and 4channels 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.
 #INPAINT_FSR_BEST or #INPAINT_FSR_FAST: 1channel grayscale or 3channel BGR image.
mask
 mask (#CV_8UC1), where nonzero pixels indicate valid image area, while zero pixels indicate area to be inpainteddst
 destination imagealgorithmType
 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 threechannel 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 2size+1dynRatio
 image is divided by dynRatio before histogram processingcode
 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 threechannel 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 2size+1dynRatio
 image is divided by dynRatio before histogram processing

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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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

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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2.searchWindowSize
 Size in pixels of the window that is used to perform blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 blockmatching. 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise. Parameters:
src
 Input 8bit or 16bit 1channel 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

createSimpleWB
public static SimpleWB createSimpleWB()
Creates an instance of SimpleWB Returns:
 automatically generated

createGrayworldWB
public static GrayworldWB createGrayworldWB()
Creates an instance of GrayworldWB Returns:
 automatically generated

createLearningBasedWB
public static LearningBasedWB createLearningBasedWB(java.lang.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

applyChannelGains
public static void applyChannelGains(Mat src, Mat dst, float gainB, float gainG, float gainR)
Implements an efficient fixedpoint approximation for applying channel gains, which is the last step of multiple white balance algorithms. Parameters:
src
 Input threechannel 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 channelgainG
 gain for the G channelgainR
 gain for the R channel

dctDenoising
public static void dctDenoising(Mat src, Mat dst, double sigma, int psize)
The function implements simple dctbased denoising <http://www.ipol.im/pub/art/2011/ysdct/>. Parameters:
src
 source imagedst
 destination imagesigma
 expected noise standard deviationpsize
 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 dctbased denoising <http://www.ipol.im/pub/art/2011/ysdct/>. Parameters:
src
 source imagedst
 destination imagesigma
 expected noise standard deviation SEE: fastNlMeansDenoising

createTonemapDurand
public static TonemapDurand createTonemapDurand(float gamma, float contrast, float saturation, float sigma_color, 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 createTonemapcontrast
 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 createTonemapDragosigma_color
 bilateral filter sigma in color spacesigma_space
 bilateral filter sigma in coordinate space Returns:
 automatically generated

createTonemapDurand
public static TonemapDurand createTonemapDurand(float gamma, float contrast, float saturation, 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 createTonemapcontrast
 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 createTonemapDragosigma_color
 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 createTonemapcontrast
 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 createTonemapcontrast
 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
