Addition of per-channel h-values for fastNlMeansDenoising[Multi][Abs]

diff --git a/modules/photo/include/opencv2/photo.hpp b/modules/photo/include/opencv2/photo.hpp
index 446e817..5e11333 100644 (file)
--- a/modules/photo/include/opencv2/photo.hpp
+++ b/modules/photo/include/opencv2/photo.hpp
@@ -138,6 +138,31 @@ parameter.
 CV_EXPORTS_W void fastNlMeansDenoising( 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
+
+@param src Input 8-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, one per
+channel. 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 fastNlMeansDenoising( InputArray src, OutputArray dst, 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
@@ -163,6 +188,33 @@ 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, one per
+channel. 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,
+        int templateWindowSize = 7, int searchWindowSize = 21);
+
 /** @brief Modification of fastNlMeansDenoising function for colored images
 
 @param src Input 8-bit 3-channel image.
@@ -204,14 +256,42 @@ 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 for luminance component. Bigger h value perfectly
-removes noise but also removes image details, smaller h value preserves details but also preserves
-some noise
+@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 fastNlMeansDenoisingMulti( 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>
+
+@param srcImgs Input 8-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, one for each
+channel. 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 fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
+        int imgToDenoiseIndex, int temporalWindowSize,
+        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
@@ -235,14 +315,46 @@ 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 for luminance component. Bigger h value perfectly
-removes noise but also removes image details, smaller h value preserves details but also preserves
-some noise
+@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, one for each
+channel. 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, 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

diff --git a/modules/photo/src/denoising.cpp b/modules/photo/src/denoising.cpp
index 30f638d..9f63254 100644 (file)
--- a/modules/photo/src/denoising.cpp
+++ b/modules/photo/src/denoising.cpp
@@ -90,6 +90,51 @@ void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
     }
 }
 
+void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float *h,
+                               int templateWindowSize, int searchWindowSize)
+{
+    Size src_size = _src.size();
+    CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()) &&
+               src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
+               ocl_fastNlMeansDenoising(_src, _dst, h, CV_MAT_CN(_src.type()),
+                                        templateWindowSize, searchWindowSize, false))
+
+    Mat src = _src.getMat();
+    _dst.create(src_size, src.type());
+    Mat dst = _dst.getMat();
+
+#ifdef HAVE_TEGRA_OPTIMIZATION
+    if(tegra::fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize))
+        return;
+#endif
+
+    switch (src.type()) {
+        case CV_8U:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<uchar, int, unsigned, DistSquared, int>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC2:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec2b, int, unsigned, DistSquared, Vec2i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC3:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec3b, int, unsigned, DistSquared, Vec3i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC4:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec4b, int, unsigned, DistSquared, Vec4i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        default:
+            CV_Error(Error::StsBadArg,
+                "Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3 and CV_8UC4 are supported");
+    }
+}
+
 void cv::fastNlMeansDenoisingAbs( InputArray _src, OutputArray _dst, float h,
                                   int  templateWindowSize, int searchWindowSize)
 {
@@ -150,6 +195,66 @@ void cv::fastNlMeansDenoisingAbs( InputArray _src, OutputArray _dst, float h,
     }
 }
 
+void cv::fastNlMeansDenoisingAbs( InputArray _src, OutputArray _dst, float *h,
+                                  int  templateWindowSize, int searchWindowSize)
+{
+    Size src_size = _src.size();
+    CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()) &&
+               src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
+               ocl_fastNlMeansDenoising(_src, _dst, h, CV_MAT_CN(_src.type()),
+                                        templateWindowSize, searchWindowSize, true))
+
+    Mat src = _src.getMat();
+    _dst.create(src_size, src.type());
+    Mat dst = _dst.getMat();
+
+    switch (src.type()) {
+        case CV_8U:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<uchar, int, unsigned, DistAbs, int>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC2:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec2b, int, unsigned, DistAbs, Vec2i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC3:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec3b, int, unsigned, DistAbs, Vec3i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC4:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec4b, int, unsigned, DistAbs, Vec4i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16U:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<ushort, int64, uint64, DistAbs, int>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16UC2:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec<ushort, 2>, int64, uint64, DistAbs, Vec2i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16UC3:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec<ushort, 3>, int64, uint64, DistAbs, Vec3i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16UC4:
+            parallel_for_(cv::Range(0, src.rows),
+                    FastNlMeansDenoisingInvoker<cv::Vec<ushort, 4>, int64, uint64, DistAbs, Vec4i>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        default:
+            CV_Error(Error::StsBadArg,
+                "Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3, CV_8UC4, CV_16U, CV_16UC2, CV_16UC3 and CV_16UC4 are supported");
+    }
+}
+
 void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
                                       float h, float hForColorComponents,
                                       int templateWindowSize, int searchWindowSize)
@@ -269,6 +374,52 @@ void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _ds
     }
 }
 
+void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
+                                    int imgToDenoiseIndex, int temporalWindowSize,
+                                    float *h, int templateWindowSize, int searchWindowSize)
+{
+    std::vector<Mat> srcImgs;
+    _srcImgs.getMatVector(srcImgs);
+
+    fastNlMeansDenoisingMultiCheckPreconditions(
+        srcImgs, imgToDenoiseIndex,
+        temporalWindowSize, templateWindowSize, searchWindowSize);
+
+    _dst.create(srcImgs[0].size(), srcImgs[0].type());
+    Mat dst = _dst.getMat();
+
+    switch (srcImgs[0].type())
+    {
+        case CV_8U:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<uchar, int, unsigned, DistSquared, int>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC2:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec2b, int, unsigned, DistSquared, Vec2i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC3:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec3b, int, unsigned, DistSquared, Vec3i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC4:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec4b, int, unsigned, DistSquared, Vec4i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        default:
+            CV_Error(Error::StsBadArg,
+                "Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3 and CV_8UC4 are supported");
+    }
+}
+
 void cv::fastNlMeansDenoisingMultiAbs( InputArrayOfArrays _srcImgs, OutputArray _dst,
                                        int imgToDenoiseIndex, int temporalWindowSize,
                                        float h, int templateWindowSize, int searchWindowSize)
@@ -339,6 +490,76 @@ void cv::fastNlMeansDenoisingMultiAbs( InputArrayOfArrays _srcImgs, OutputArray
     }
 }
 
+void cv::fastNlMeansDenoisingMultiAbs( InputArrayOfArrays _srcImgs, OutputArray _dst,
+                                       int imgToDenoiseIndex, int temporalWindowSize,
+                                       float *h, int templateWindowSize, int searchWindowSize)
+{
+    std::vector<Mat> srcImgs;
+    _srcImgs.getMatVector(srcImgs);
+
+    fastNlMeansDenoisingMultiCheckPreconditions(
+        srcImgs, imgToDenoiseIndex,
+        temporalWindowSize, templateWindowSize, searchWindowSize);
+
+    _dst.create(srcImgs[0].size(), srcImgs[0].type());
+    Mat dst = _dst.getMat();
+
+    switch (srcImgs[0].type())
+    {
+        case CV_8U:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<uchar, int, unsigned, DistAbs, int>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC2:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec2b, int, unsigned, DistAbs, Vec2i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC3:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec3b, int, unsigned, DistAbs, Vec3i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC4:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec4b, int, unsigned, DistAbs, Vec4i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16U:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<ushort, int64, uint64, DistAbs, int>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16UC2:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec<ushort, 2>, int64, uint64, DistAbs, Vec2i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16UC3:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec<ushort, 3>, int64, uint64, DistAbs, Vec3i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_16UC4:
+            parallel_for_(cv::Range(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec<ushort, 4>, int64, uint64, DistAbs, Vec4i>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        default:
+            CV_Error(Error::StsBadArg,
+                "Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3, CV_8UC4, CV_16U, CV_16UC2, CV_16UC3 and CV_16UC4 are supported");
+    }
+}
+
 void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
                                            int imgToDenoiseIndex, int temporalWindowSize,
                                            float h, float hForColorComponents,

diff --git a/modules/photo/src/fast_nlmeans_denoising_invoker.hpp b/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
index ff35550..6e74acf 100644 (file)
--- a/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
+++ b/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
@@ -236,7 +236,7 @@ void FastNlMeansDenoisingInvoker<T, IT, UIT, D, WT>::operator() (const Range& ra
                 for (int x = 0; x < search_window_size_; x++)
                 {
                     int almostAvgDist = dist_sums_row[x] >> almost_template_window_size_sq_bin_shift_;
-                    int weight = almost_dist2weight_[almostAvgDist];
+                    WT weight = almost_dist2weight_[almostAvgDist];
                     T p = cur_row_ptr[border_size_ + search_window_x + x];
                     incWithWeight<T, IT, WT>(estimation, weights_sum, weight, p);
                 }

diff --git a/modules/photo/src/fast_nlmeans_denoising_invoker_commons.hpp b/modules/photo/src/fast_nlmeans_denoising_invoker_commons.hpp
index efd482f..9833ea7 100644 (file)
--- a/modules/photo/src/fast_nlmeans_denoising_invoker_commons.hpp
+++ b/modules/photo/src/fast_nlmeans_denoising_invoker_commons.hpp
@@ -387,7 +387,7 @@ template <typename ET, typename IT, typename EW> struct incWithWeight_<Vec<ET, 4
 };
 
 template <typename T, typename IT, typename WT>
-static inline void incWithWeight(IT* estimation, IT* weights_sum, IT weight, T p)
+static inline void incWithWeight(IT* estimation, IT* weights_sum, WT weight, T p)
 {
     return incWithWeight_<T, IT, WT>::f(estimation, weights_sum, weight, p);
 }

diff --git a/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp b/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
index cd3833a..3f13f40 100644 (file)
--- a/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
+++ b/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
@@ -262,7 +262,7 @@ void FastNlMeansMultiDenoisingInvoker<T, IT, UIT, D, WT>::operator() (const Rang
                     {
                         int almostAvgDist = dist_sums_row[x] >> almost_template_window_size_sq_bin_shift;
 
-                        int weight =  almost_dist2weight[almostAvgDist];
+                        WT weight =  almost_dist2weight[almostAvgDist];
                         T p = cur_row_ptr[border_size_ + search_window_x + x];
                         incWithWeight<T, IT, WT>(estimation, weights_sum, weight, p);
                     }

diff --git a/modules/photo/src/opencl/nlmeans.cl b/modules/photo/src/opencl/nlmeans.cl
index 936aed6..879665f 100644 (file)
--- a/modules/photo/src/opencl/nlmeans.cl
+++ b/modules/photo/src/opencl/nlmeans.cl
@@ -36,7 +36,7 @@ __kernel void calcAlmostDist2Weight(__global wlut_t * almostDist2Weight, int alm
 #endif
         wlut_t weight = convert_wlut_t(fixedPointMult * (isnan(w) ? (w_t)1.0 : w));
         almostDist2Weight[almostDist] =
-            weight < WEIGHT_THRESHOLD * fixedPointMult ? (wlut_t)0 : weight;
+            weight < (wlut_t)(WEIGHT_THRESHOLD * fixedPointMult) ? (wlut_t)0 : weight;
     }
 }