org.opencv.xphoto

Class Xphoto

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_FSR_BEST
static int	INPAINT_FSR_FAST
static int	INPAINT_SHIFTMAP

Constructor Summary

Constructors

Constructor and Description
Xphoto()

Method Summary

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_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 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

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

Constructor Detail

Xphoto

public Xphoto()

Method Detail

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

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

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 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 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.
• #INPAINT_FSR_BEST or #INPAINT_FSR_FAST: 1-channel grayscale or 3-channel BGR image.

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

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 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_color - bilateral filter sigma in color space

sigma_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 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_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 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

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(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 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