diff --git a/modules/photo/src/fast_nlmeans_denoising_invoker.hpp b/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
index 1dcb6b3ece..c4f13826d2 100644
--- a/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
+++ b/modules/photo/src/fast_nlmeans_denoising_invoker.hpp
@@ -257,7 +257,7 @@ void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) cons
             }
 
             for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
-                estimation[channel_num] = (estimation[channel_num] + weights_sum/2) / weights_sum;
+                estimation[channel_num] = ((unsigned)estimation[channel_num] + weights_sum/2) / weights_sum;
 
             dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
         }
diff --git a/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp b/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
index 870760b48b..2ae5054e00 100644
--- a/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
+++ b/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
@@ -287,7 +287,7 @@ void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range)
             }
 
             for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
-                estimation[channel_num] = (estimation[channel_num] + weights_sum / 2) / weights_sum;
+                estimation[channel_num] = ((unsigned)estimation[channel_num] + weights_sum / 2) / weights_sum;
 
             dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
 
diff --git a/modules/photo/test/test_denoising.cpp b/modules/photo/test/test_denoising.cpp
index 7312bbbafa..55e876c635 100644
--- a/modules/photo/test/test_denoising.cpp
+++ b/modules/photo/test/test_denoising.cpp
@@ -56,7 +56,7 @@ using namespace std;
 #endif
 
 
-TEST(Imgproc_DenoisingGrayscale, regression)
+TEST(Photo_DenoisingGrayscale, regression)
 {
     string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
     string original_path = folder + "lena_noised_gaussian_sigma=10.png";
@@ -76,7 +76,7 @@ TEST(Imgproc_DenoisingGrayscale, regression)
     ASSERT_EQ(0, norm(result != expected));
 }
 
-TEST(Imgproc_DenoisingColored, regression)
+TEST(Photo_DenoisingColored, regression)
 {
     string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
     string original_path = folder + "lena_noised_gaussian_sigma=10.png";
@@ -96,7 +96,7 @@ TEST(Imgproc_DenoisingColored, regression)
     ASSERT_EQ(0, norm(result != expected));
 }
 
-TEST(Imgproc_DenoisingGrayscaleMulti, regression)
+TEST(Photo_DenoisingGrayscaleMulti, regression)
 {
     const int imgs_count = 3;
     string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
@@ -121,7 +121,7 @@ TEST(Imgproc_DenoisingGrayscaleMulti, regression)
     ASSERT_EQ(0, norm(result != expected));
 }
 
-TEST(Imgproc_DenoisingColoredMulti, regression)
+TEST(Photo_DenoisingColoredMulti, regression)
 {
     const int imgs_count = 3;
     string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
@@ -146,3 +146,13 @@ TEST(Imgproc_DenoisingColoredMulti, regression)
     ASSERT_EQ(0, norm(result != expected));
 }
 
+TEST(Photo_White, issue_2646)
+{
+    cv::Mat img(50, 50, CV_8UC1, cv::Scalar::all(255));
+    cv::Mat filtered;
+    cv::fastNlMeansDenoising(img, filtered);
+
+    int nonWhitePixelsCount = (int)img.total() - cv::countNonZero(filtered == img);
+
+    ASSERT_EQ(0, nonWhitePixelsCount);
+}
diff --git a/modules/photo/test/test_inpaint.cpp b/modules/photo/test/test_inpaint.cpp
index 8181c1ca56..26a997e29d 100644
--- a/modules/photo/test/test_inpaint.cpp
+++ b/modules/photo/test/test_inpaint.cpp
@@ -115,4 +115,4 @@ void CV_InpaintTest::run( int )
     ts->set_failed_test_info(cvtest::TS::OK);
 }
 
-TEST(Imgproc_Inpaint, regression) { CV_InpaintTest test; test.safe_run(); }
+TEST(Photo_Inpaint, regression) { CV_InpaintTest test; test.safe_run(); }