diff --git a/modules/photo/src/arrays.hpp b/modules/photo/src/arrays.hpp
index c1c4e5f97..0d86cb6e3 100644
--- a/modules/photo/src/arrays.hpp
+++ b/modules/photo/src/arrays.hpp
@@ -67,7 +67,7 @@ template <class T> struct Array2d {
 
     ~Array2d() {
         if (needToDeallocArray) {
-            delete a;
+            delete[] a;
         }
     }
 
@@ -96,7 +96,7 @@ template <class T> struct Array3d {
 
     ~Array3d() {
         if (needToDeallocArray) {
-            delete a;
+            delete[] a;
         }
     }
 
@@ -138,7 +138,7 @@ template <class T> struct Array4d {
 
     ~Array4d() {
         if (needToDeallocArray) {
-            delete a;
+            delete[] a;
         }
     }
 
diff --git a/modules/photo/src/denoising.cpp b/modules/photo/src/denoising.cpp
index 39643e33d..b980b407b 100644
--- a/modules/photo/src/denoising.cpp
+++ b/modules/photo/src/denoising.cpp
@@ -103,7 +103,7 @@ static void fastNlMeansDenoisingMultiCheckPreconditions(
                                int imgToDenoiseIndex, int temporalWindowSize,
                                int templateWindowSize, int searchWindowSize) 
 {
-    int src_imgs_size = srcImgs.size();
+    int src_imgs_size = (int)srcImgs.size();
     if (src_imgs_size == 0) {
         CV_Error(CV_StsBadArg, "Input images vector should not be empty!");
     }
@@ -176,7 +176,7 @@ void cv::fastNlMeansDenoisingColoredMulti( const std::vector<Mat>& srcImgs,
         temporalWindowSize, templateWindowSize, searchWindowSize
     );
 
-    int src_imgs_size = srcImgs.size();
+    int src_imgs_size = (int)srcImgs.size();
 
     if (srcImgs[0].type() != CV_8UC3) {
         CV_Error(CV_StsBadArg, "Type of input images should be CV_8UC3!");
diff --git a/modules/photo/src/fast_nlmeans_denoising_invoker.hpp b/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
index 58e4a45e1..6724e8272 100644
--- a/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
+++ b/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
@@ -62,6 +62,10 @@ struct FastNlMeansDenoisingInvoker {
 
         void operator() (const BlockedRange& range) const;
 
+		void operator= (const FastNlMeansDenoisingInvoker& invoker) {
+			CV_Error(CV_StsNotImplemented, "Assigment operator is not implemented");
+		}
+
     private:
         const Mat& src_;
         Mat& dst_;
@@ -102,6 +106,8 @@ FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
     int search_window_size, 
     const double h) : src_(src), dst_(dst)
 {
+    CV_Assert(src.channels() <= 3);
+
     template_window_half_size_ = template_window_size / 2;
     search_window_half_size_ = search_window_size / 2;
     template_window_size_ = template_window_half_size_ * 2 + 1;
@@ -153,16 +159,12 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
     int row_from = range.begin();
     int row_to = range.end() - 1;
 
-    int dist_sums_array[search_window_size_ * search_window_size_];
-    Array2d<int> dist_sums(dist_sums_array, search_window_size_, search_window_size_);
+    Array2d<int> dist_sums(search_window_size_, search_window_size_);
     
     // for lazy calc optimization
-    int col_dist_sums_array[template_window_size_ * search_window_size_ * search_window_size_];
-    Array3d<int> col_dist_sums(&col_dist_sums_array[0], 
-        template_window_size_, search_window_size_, search_window_size_);
+    Array3d<int> col_dist_sums(template_window_size_, search_window_size_, search_window_size_);
     
     int first_col_num = -1;
-
     Array3d<int> up_col_dist_sums(src_.cols, search_window_size_, search_window_size_);
 
     for (int i = row_from; i <= row_to; i++) {
@@ -233,7 +235,7 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
             // calc weights
             int weights_sum = 0;
             
-            int estimation[src_.channels()];            
+            int estimation[3];            
             for (int channel_num = 0; channel_num < src_.channels(); channel_num++) {
                 estimation[channel_num] = 0;
             }
diff --git a/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp b/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
index cb08c7e43..02185d13d 100644
--- a/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
+++ b/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
@@ -63,6 +63,10 @@ struct FastNlMeansMultiDenoisingInvoker {
 
         void operator() (const BlockedRange& range) const;
 
+		void operator= (const FastNlMeansMultiDenoisingInvoker& invoker) {
+			CV_Error(CV_StsNotImplemented, "Assigment operator is not implemented");
+		}
+
     private:
         int rows_;
         int cols_;
@@ -111,6 +115,9 @@ FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
     int search_window_size, 
     const double h) : dst_(dst), extended_srcs_(srcImgs.size())
 {
+    CV_Assert(srcImgs.size() > 0);
+    CV_Assert(srcImgs[0].channels() <= 3);
+
     rows_ = srcImgs[0].rows;
     cols_ = srcImgs[0].cols;
     channels_count_ = srcImgs[0].channels();
@@ -175,16 +182,11 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
     int row_from = range.begin();
     int row_to = range.end() - 1;
 
-    int dist_sums_array[temporal_window_size_ * search_window_size_ * search_window_size_];
-    Array3d<int> dist_sums(dist_sums_array, 
-        temporal_window_size_, search_window_size_, search_window_size_);
+    Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
     
     // for lazy calc optimization
-    int col_dist_sums_array[
-        template_window_size_ * temporal_window_size_ * search_window_size_ * search_window_size_];
-
-    Array4d<int> col_dist_sums(col_dist_sums_array, 
-        template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
+    Array4d<int> col_dist_sums(
+		template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
     
     int first_col_num = -1;
 
@@ -263,7 +265,7 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
             // calc weights
             int weights_sum = 0;
             
-            int estimation[channels_count_];            
+            int estimation[3];            
             for (int channel_num = 0; channel_num < channels_count_; channel_num++) {
                 estimation[channel_num] = 0;
             }