diff --git a/modules/ximgproc/include/opencv2/ximgproc/weighted_median_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/weighted_median_filter.hpp
index 30a169cd8..d3da29efd 100644
--- a/modules/ximgproc/include/opencv2/ximgproc/weighted_median_filter.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/weighted_median_filter.hpp
@@ -63,12 +63,12 @@ namespace ximgproc
 */
 enum WMFWeightType
 {
-    WMF_EXP, //!< \f$exp(-|I1-I2|^2/(2*sigma^2))\f$
-    WMF_IV1, //!< \f$(|I1-I2|+sigma)^-1\f$
-    WMF_IV2, //!< \f$(|I1-I2|^2+sigma^2)^-1\f$
-    WMF_COS, //!< \f$dot(I1,I2)/(|I1|*|I2|)\f$
-    WMF_JAC, //!< \f$(min(r1,r2)+min(g1,g2)+min(b1,b2))/(max(r1,r2)+max(g1,g2)+max(b1,b2))\f$
-    WMF_OFF //!< unweighted
+    WMF_EXP = 1     , //!< \f$exp(-|I1-I2|^2/(2*sigma^2))\f$
+    WMF_IV1 = 1 << 1, //!< \f$(|I1-I2|+sigma)^-1\f$
+    WMF_IV2 = 1 << 2, //!< \f$(|I1-I2|^2+sigma^2)^-1\f$
+    WMF_COS = 1 << 3, //!< \f$dot(I1,I2)/(|I1|*|I2|)\f$
+    WMF_JAC = 1 << 4, //!< \f$(min(r1,r2)+min(g1,g2)+min(b1,b2))/(max(r1,r2)+max(g1,g2)+max(b1,b2))\f$
+    WMF_OFF = 1 << 5  //!< unweighted
 };
 
 /**
@@ -87,7 +87,8 @@ enum WMFWeightType
 *
 * @sa medianBlur, jointBilateralFilter
 */
-CV_EXPORTS void weightedMedianFilter(InputArray joint, InputArray src, OutputArray dst, int r, double sigma=25.5, WMFWeightType weightType=WMF_EXP, Mat mask=Mat());
+CV_EXPORTS_W void weightedMedianFilter(InputArray joint, InputArray src, OutputArray dst,
+                                       int r, double sigma = 25.5, int weightType = WMF_EXP, InputArray mask = noArray());
 }
 }
 
diff --git a/modules/ximgproc/src/weighted_median_filter.cpp b/modules/ximgproc/src/weighted_median_filter.cpp
index 358959cdd..d438fb962 100644
--- a/modules/ximgproc/src/weighted_median_filter.cpp
+++ b/modules/ximgproc/src/weighted_median_filter.cpp
@@ -231,7 +231,7 @@ inline void updateBCB(int &num,int *f,int *b,int i,int v)
  *                If F is 3-channel, perform k-means clustering
  *                If F is 1-channel, only perform type-casting
  ***************************************************************/
-void featureIndexing(Mat &F, float **&wMap, int &nF, float sigmaI, WMFWeightType weightType){
+void featureIndexing(Mat &F, float **&wMap, int &nF, float sigmaI, int weightType){
     // Configuration and Declaration
     Mat FNew;
     int cols = F.cols, rows = F.rows;
@@ -493,7 +493,7 @@ Mat filterCore(Mat &I, Mat &F, float **wMap, int r=20, int nF=256, int nI=256, M
             // Move cut-point to the left
             if(balanceWeight >= 0)
             {
-                for(;balanceWeight >= 0 && curMedianVal; curMedianVal--)
+                for(;balanceWeight >= 0 && curMedianVal > 0; curMedianVal--)
                 {
                     float curWeight = 0;
                     int *nextHist = H[curMedianVal];
@@ -539,8 +539,13 @@ Mat filterCore(Mat &I, Mat &F, float **wMap, int r=20, int nF=256, int nI=256, M
             }
 
             // Weighted median is found and written to the output image
-            if(balanceWeight<0)outImg.ptr<int>(y,x)[0] = curMedianVal+1;
-            else outImg.ptr<int>(y,x)[0] = curMedianVal;
+            if(curMedianVal != -1)
+            {
+                if(balanceWeight < 0)
+                    outImg.ptr<int>(y,x)[0] = curMedianVal+1;
+                else
+                    outImg.ptr<int>(y,x)[0] = curMedianVal;
+            }
 
             // Update joint-histogram and BCB when local window is shifted.
             int fval,gval,*curHist;
@@ -636,7 +641,7 @@ namespace cv
 {
 namespace ximgproc
 {
-void weightedMedianFilter(InputArray joint, InputArray src, OutputArray dst, int r, double sigma, WMFWeightType weightType, Mat mask)
+void weightedMedianFilter(InputArray joint, InputArray src, OutputArray dst, int r, double sigma, int weightType, InputArray mask)
 {
     CV_Assert(!src.empty());
     CV_Assert(r > 0 && sigma > 0);
@@ -697,7 +702,7 @@ void weightedMedianFilter(InputArray joint, InputArray src, OutputArray dst, int
     //Filtering - Joint-Histogram Framework
     for(int i=0; i<(int)Is.size(); i++)
     {
-        Is[i] = filterCore(Is[i], F, wMap, r, nF,nI,mask);
+        Is[i] = filterCore(Is[i], F, wMap, r, nF, nI, mask.getMat());
     }
     float2D_release(wMap);
 
diff --git a/modules/ximgproc/test/test_weighted_median_filter.cpp b/modules/ximgproc/test/test_weighted_median_filter.cpp
index 9dea51fd1..69a653047 100644
--- a/modules/ximgproc/test/test_weighted_median_filter.cpp
+++ b/modules/ximgproc/test/test_weighted_median_filter.cpp
@@ -102,6 +102,16 @@ TEST(WeightedMedianFilterTest, ReferenceAccuracy)
     EXPECT_LE(cvtest::norm(res, ref, NORM_L2), totalMaxError);
 }
 
+TEST(WeightedMedianFilterTest, mask_zeros_no_crash)
+{
+    Mat img = imread(getDataDir() + "cv/ximgproc/sources/01.png");
+    Mat mask = Mat::zeros(img.size(), CV_8U);
+    Mat filtered;
+    weightedMedianFilter(img, img, filtered, 3, 20, WMF_EXP, mask);
+
+    EXPECT_EQ(cv::norm(img, filtered, NORM_INF), 0.0);
+}
+
 INSTANTIATE_TEST_CASE_P(TypicalSET, WeightedMedianFilterTest, Combine(Values(szODD, szQVGA),  Values(WMF_EXP, WMF_IV2, WMF_OFF)));
 
 }