diff --git a/modules/imgproc/test/test_adpativethresh.cpp b/modules/imgproc/test/test_adpativethresh.cpp
new file mode 100644
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+++ b/modules/imgproc/test/test_adpativethresh.cpp
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+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include <string>
+
+using namespace cv;
+using namespace std;
+
+class CV_Adaptivethresh : public cvtest::BaseTest
+{
+public:
+    CV_Adaptivethresh();
+    ~CV_Adaptivethresh();
+protected:
+    void run(int);
+};
+
+CV_Adaptivethresh::CV_Adaptivethresh() {}
+CV_Adaptivethresh::~CV_Adaptivethresh() {}
+
+void CV_Adaptivethresh::run( int /* start_from */)
+{
+    string exp_path = string(ts->get_data_path()) + "adaptivethresh/lena_orig.png";
+    Mat lena = imread(exp_path, 0); // CV_LOAD_IMAGE_GRAYSCALE=0
+    if (lena.empty() )
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
+        return;
+    }
+    int sum=0;
+    for (int i = 0; i < lena.rows; i++)
+    {
+        unsigned char *ptr = lena.ptr(i);
+        for (int j=0;j<lena.cols;j++,ptr++)
+            sum+=*ptr;
+    }
+    if (sum!=31910861)
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
+        return;
+    }
+    int windowSize[9] = {3,9,11,17,21,25,29,37,47};
+    int expectedValueMean[9] = {96138,121836,124499,129096,130538,131330,131743,131616,131223};
+    int expectedValueGaussNew[9] = {86308,112910,116197,122117,124672,126488,127855,129377,130387};
+    int expectedValueGaussOld[9] = {88583,81365,154081,98049,149357,106414,179701,168433,90250};
+    Mat im;
+    bool failed=false;
+    for(int i = 0; i<9; ++i )
+    {
+        adaptiveThreshold( lena, im, 255,cv::ADAPTIVE_THRESH_MEAN_C,THRESH_BINARY,windowSize[i],0);
+        int numberWhite=countNonZero(im);
+        if (numberWhite != expectedValueMean[i])
+        {
+            ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+            return;
+        }
+        adaptiveThreshold( lena, im, 255,cv::ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,windowSize[i],0);
+        if (numberWhite != expectedValueGaussNew[i])
+        {   
+
+            if (numberWhite != expectedValueGaussOld[i])
+                ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
+            else
+                ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+        }
+    }
+    if (failed)
+        ts->set_failed_test_info(cvtest::TS::OK);
+    else
+        ts->set_failed_test_info(cvtest::TS::OK);
+}
+
+TEST(Imgproc_Adaptivethresh, regression) { CV_Adaptivethresh test; test.safe_run(); }