Removed fastNlMeansDenoisingColored[Multi]Abs

modules/photo/include/opencv2/photo.hpp

@@ -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

modules/photo/test/ocl/test_denoising.cpp

@@ -103,36 +103,6 @@ OCL_TEST_P(FastNlMeansDenoising_hsep, Mat)
     }
 }
 
-typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingAbs;
-
-OCL_TEST_P(FastNlMeansDenoisingAbs, Mat)
-{
-    for (int j = 0; j < test_loop_times; j++)
-    {
-        generateTestData();
-
-        OCL_OFF(cv::fastNlMeansDenoisingAbs(src_roi, dst_roi, h[0], templateWindowSize, searchWindowSize));
-        OCL_ON(cv::fastNlMeansDenoisingAbs(usrc_roi, udst_roi, h[0], templateWindowSize, searchWindowSize));
-
-        OCL_EXPECT_MATS_NEAR(dst, 1);
-    }
-}
-
-typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingAbs_hsep;
-
-OCL_TEST_P(FastNlMeansDenoisingAbs_hsep, Mat)
-{
-    for (int j = 0; j < test_loop_times; j++)
-    {
-        generateTestData();
-
-        OCL_OFF(cv::fastNlMeansDenoisingAbs(src_roi, dst_roi, h, templateWindowSize, searchWindowSize));
-        OCL_ON(cv::fastNlMeansDenoisingAbs(usrc_roi, udst_roi, h, templateWindowSize, searchWindowSize));
-
-        OCL_EXPECT_MATS_NEAR(dst, 1);
-    }
-}
-
 typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingColored;
 
 OCL_TEST_P(FastNlMeansDenoisingColored, Mat)
@@ -152,10 +122,6 @@ OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoising,
                             Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
 OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoising_hsep,
                             Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
-OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingAbs,
-                            Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
-OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingAbs_hsep,
-                            Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
 OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingColored,
                             Combine(Values(3, 4), Bool(), Values(false)));