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Removed fastNlMeansDenoisingColored[Multi]Abs

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Erik Karlsson
2015-03-23 23:23:35 +01:00
parent 82c54104d6
commit 9fff7896c5
3 changed files with 1 additions and 369 deletions
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118
modules/photo/include/opencv2/photo.hpp
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@@ -160,63 +160,10 @@ image in different colorspaces. Such approach is used in fastNlMeansDenoisingCol
image to CIELAB colorspace and then separately denoise L and AB components with different h
parameter.
*/
CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
const std::vector<float>& h,
int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Perform image denoising using Non-local Means Denoising
algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/>
with several computational optimizations. Noise expected to be a
gaussian white noise. Uses squared sum of absolute value distances
instead of sum of squared distances for weight calculation
@param src Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel or 4-channel image.
@param dst Output image with the same size and type as src .
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
removes image details, smaller h value preserves details but also preserves some noise
This function expected to be applied to grayscale images. For colored images look at
fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
image to CIELAB colorspace and then separately denoise L and AB components with different h
parameter.
*/
CV_EXPORTS_W void fastNlMeansDenoisingAbs( InputArray src, OutputArray dst, float h = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Perform image denoising using Non-local Means Denoising
algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/>
with several computational optimizations. Noise expected to be a
gaussian white noise. Uses squared sum of absolute value distances
instead of sum of squared distances for weight calculation
@param src Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel or 4-channel image.
@param dst Output image with the same size and type as src .
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Array of parameters regulating filter strength, either one
parameter applied to all channels or one per channel in dst. Big h value
perfectly removes noise but also removes image details, smaller h
value preserves details but also preserves some noise
This function expected to be applied to grayscale images. For colored images look at
fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
image to CIELAB colorspace and then separately denoise L and AB components with different h
parameter.
*/
CV_EXPORTS_W void fastNlMeansDenoisingAbs( InputArray src, OutputArray dst,
const std::vector<float>& h,
int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Modification of fastNlMeansDenoising function for colored images
@param src Input 8-bit 3-channel image.
@@ -294,69 +241,6 @@ CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputA
int imgToDenoiseIndex, int temporalWindowSize,
const std::vector<float>& h , int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Modification of fastNlMeansDenoising function for images
sequence where consequtive images have been captured in small period
of time. For example video. This version of the function is for
grayscale images or for manual manipulation with colorspaces. For more
details see
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>. Uses
squared sum of absolute value distances instead of sum of squared
distances for weight calculation
@param srcImgs Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel
or 4-channel images sequence. All images should have the same type and
size.
@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
srcImgs[imgToDenoiseIndex] image.
@param dst Output image with the same size and type as srcImgs images.
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength. Bigger h value
perfectly removes noise but also removes image details, smaller h
value preserves details but also preserves some noise
*/
CV_EXPORTS_W void fastNlMeansDenoisingMultiAbs( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Modification of fastNlMeansDenoising function for images
sequence where consequtive images have been captured in small period
of time. For example video. This version of the function is for
grayscale images or for manual manipulation with colorspaces. For more
details see
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>. Uses
squared sum of absolute value distances instead of sum of squared
distances for weight calculation
@param srcImgs Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel
or 4-channel images sequence. All images should have the same type and
size.
@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
srcImgs[imgToDenoiseIndex] image.
@param dst Output image with the same size and type as srcImgs images.
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Array of parameters regulating filter strength, either one
parameter applied to all channels or one per channel in dst. Big h value
perfectly removes noise but also removes image details, smaller h
value preserves details but also preserves some noise
*/
CV_EXPORTS_W void fastNlMeansDenoisingMultiAbs( InputArrayOfArrays srcImgs, OutputArray dst,
int imgToDenoiseIndex, int temporalWindowSize,
const std::vector<float>& h, int templateWindowSize = 7, int searchWindowSize = 21);
/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and