diff --git a/modules/gpu/src/arithm.cpp b/modules/gpu/src/arithm.cpp
index 5f7dd6150d..8e9a2151f2 100644
--- a/modules/gpu/src/arithm.cpp
+++ b/modules/gpu/src/arithm.cpp
@@ -486,10 +486,10 @@ void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode)
 namespace cv { namespace gpu { namespace mathfunc
 {
     template <typename T>
-    void sum_caller(const DevMem2D src, PtrStep buf, double* sum);
+    void sum_caller(const DevMem2D src, PtrStep buf, double* sum, int cn);
 
     template <typename T>
-    void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum);
+    void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum, int cn);
 
     template <typename T>
     void sqsum_caller(const DevMem2D src, PtrStep buf, double* sum);
@@ -499,7 +499,7 @@ namespace cv { namespace gpu { namespace mathfunc
 
     namespace sum
     {
-        void get_buf_size_required(int cols, int rows, int& bufcols, int& bufrows);
+        void get_buf_size_required(int cols, int rows, int cn, int& bufcols, int& bufrows);
     }
 }}}
 
@@ -512,27 +512,26 @@ Scalar cv::gpu::sum(const GpuMat& src)
 Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf) 
 {
     using namespace mathfunc;
-    CV_Assert(src.channels() == 1);
 
-    typedef void (*Caller)(const DevMem2D, PtrStep, double*);
+    typedef void (*Caller)(const DevMem2D, PtrStep, double*, int);
     static const Caller callers[2][7] = 
         { { sum_multipass_caller<unsigned char>, sum_multipass_caller<char>, 
             sum_multipass_caller<unsigned short>, sum_multipass_caller<short>, 
             sum_multipass_caller<int>, sum_multipass_caller<float>, 0 },
           { sum_caller<unsigned char>, sum_caller<char>, 
             sum_caller<unsigned short>, sum_caller<short>, 
-            sum_caller<int>, sum_caller<float>, sum_caller<double> } };
+            sum_caller<int>, sum_caller<float>, 0 } };
 
     Size bufSize;
-    sum::get_buf_size_required(src.cols, src.rows, bufSize.width, bufSize.height); 
+    sum::get_buf_size_required(src.cols, src.rows, src.channels(), bufSize.width, bufSize.height); 
     buf.create(bufSize, CV_8U);
 
-    Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
+    Caller caller = callers[hasAtomicsSupport(getDevice())][src.depth()];
     if (!caller) CV_Error(CV_StsBadArg, "sum: unsupported type");
 
-    double result;
-    caller(src, buf, &result);
-    return result;
+    double result[4];
+    caller(src, buf, result, src.channels());
+    return Scalar(result[0], result[1], result[2], result[3]);
 }
 
 Scalar cv::gpu::sqrSum(const GpuMat& src) 
@@ -553,10 +552,10 @@ Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
             sqsum_multipass_caller<int>, sqsum_multipass_caller<float>, 0 },
           { sqsum_caller<unsigned char>, sqsum_caller<char>, 
             sqsum_caller<unsigned short>, sqsum_caller<short>, 
-            sqsum_caller<int>, sqsum_caller<float>, sqsum_caller<double> } };
+            sqsum_caller<int>, sqsum_caller<float>, 0 } };
 
     Size bufSize;
-    sum::get_buf_size_required(src.cols, src.rows, bufSize.width, bufSize.height); 
+    sum::get_buf_size_required(src.cols, src.rows, 1, bufSize.width, bufSize.height); 
     buf.create(bufSize, CV_8U);
 
     Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
diff --git a/modules/gpu/src/cuda/mathfunc.cu b/modules/gpu/src/cuda/mathfunc.cu
index c990228541..d8b59d2f15 100644
--- a/modules/gpu/src/cuda/mathfunc.cu
+++ b/modules/gpu/src/cuda/mathfunc.cu
@@ -42,6 +42,7 @@
 
 #include "opencv2/gpu/device/limits_gpu.hpp"
 #include "opencv2/gpu/device/saturate_cast.hpp"
+#include "opencv2/gpu/device/vecmath.hpp"
 #include "transform.hpp"
 #include "internal_shared.hpp"
 
@@ -1451,11 +1452,11 @@ namespace cv { namespace gpu { namespace mathfunc
     }
 
 
-    void get_buf_size_required(int cols, int rows, int& bufcols, int& bufrows)
+    void get_buf_size_required(int cols, int rows, int cn, int& bufcols, int& bufrows)
     {
         dim3 threads, grid;
         estimate_thread_cfg(cols, rows, threads, grid);
-        bufcols = grid.x * grid.y * sizeof(double);
+        bufcols = grid.x * grid.y * sizeof(double) * cn;
         bufrows = 1;
     }
 
@@ -1469,7 +1470,7 @@ namespace cv { namespace gpu { namespace mathfunc
     }
 
     template <typename T, typename R, typename Op, int nthreads>
-    __global__ void sum_kernel(const DevMem2D_<T> src, R* result)
+    __global__ void sum_kernel(const DevMem2D src, R* result)
     {
         __shared__ R smem[nthreads];
 
@@ -1481,7 +1482,7 @@ namespace cv { namespace gpu { namespace mathfunc
         R sum = 0;
         for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
         {
-            const T* ptr = src.ptr(y0 + y * blockDim.y);
+            const T* ptr = (const T*)src.ptr(y0 + y * blockDim.y);
             for (int x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
                 sum += Op::call(ptr[x0 + x * blockDim.x]);
         }
@@ -1539,11 +1540,116 @@ namespace cv { namespace gpu { namespace mathfunc
             result[0] = smem[0];
     }
 
+
+    template <typename T, typename R, typename Op, int nthreads>
+    __global__ void sum_kernel_C2(const DevMem2D src, typename TypeVec<R, 2>::vec_t* result)
+    {
+        typedef typename TypeVec<T, 2>::vec_t SrcType;
+        typedef typename TypeVec<R, 2>::vec_t DstType;
+
+        __shared__ R smem[nthreads * 2];
+
+        const int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
+        const int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
+        const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+        const int bid = blockIdx.y * gridDim.x + blockIdx.x;
+
+        SrcType val;
+        DstType sum = VecTraits<DstType>::all(0);
+        for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
+        {
+            const SrcType* ptr = (const SrcType*)src.ptr(y0 + y * blockDim.y);
+            for (int x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
+            {
+                val = ptr[x0 + x * blockDim.x];
+                sum = sum + VecTraits<DstType>::make(Op::call(val.x), Op::call(val.y));
+            }
+        }
+
+        smem[tid] = sum.x;
+        smem[tid + nthreads] = sum.y;
+        __syncthreads();
+
+        sum_in_smem<nthreads, R>(smem, tid);
+        sum_in_smem<nthreads, R>(smem + nthreads, tid);
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 110
+        __shared__ bool is_last;
+
+        if (tid == 0)
+        {
+            DstType res;
+            res.x = smem[0];
+            res.y = smem[nthreads];
+            result[bid] = res;
+            __threadfence();
+
+            unsigned int ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
+            is_last = (ticket == gridDim.x * gridDim.y - 1);
+        }
+
+        __syncthreads();
+
+        if (is_last)
+        {
+            DstType res = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<DstType>::all(0);
+            smem[tid] = res.x;
+            smem[tid + nthreads] = res.y;
+            __syncthreads();
+
+            sum_in_smem<nthreads, R>(smem, tid);
+            sum_in_smem<nthreads, R>(smem + nthreads, tid);
+
+            if (tid == 0) 
+            {
+                res.x = smem[0];
+                res.y = smem[nthreads];
+                result[0] = res;
+                blocks_finished = 0;
+            }
+        }
+#else
+        if (tid == 0) 
+        {
+            DstType res;
+            res.x = smem[0];
+            res.y = smem[nthreads];
+            result[bid] = res;
+        }
+#endif
+    }
+
+
+    template <typename T, typename R, int nthreads>
+    __global__ void sum_pass2_kernel_C2(typename TypeVec<R, 2>::vec_t* result, int size)
+    {
+        typedef typename TypeVec<R, 2>::vec_t DstType;
+
+        __shared__ R smem[nthreads * 2];
+
+        const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+
+        DstType res = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<DstType>::all(0);
+        smem[tid] = res.x;
+        smem[tid + nthreads] = res.y;
+        __syncthreads();
+
+        sum_in_smem<nthreads, R>(smem, tid);
+        sum_in_smem<nthreads, R>(smem + nthreads, tid);
+
+        if (tid == 0) 
+        {
+            res.x = smem[0];
+            res.y = smem[nthreads];
+            result[0] = res;
+        }
+    }
+
     } // namespace sum
 
 
     template <typename T>
-    void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum)
+    void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum, int cn)
     {
         using namespace sum;
         typedef typename SumType<T>::R R;
@@ -1552,27 +1658,40 @@ namespace cv { namespace gpu { namespace mathfunc
         estimate_thread_cfg(src.cols, src.rows, threads, grid);
         set_kernel_consts(src.cols, src.rows, threads, grid);
 
-        R* buf_ = (R*)buf.ptr(0);
-
-        sum_kernel<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>((const DevMem2D_<T>)src, buf_);
-        sum_pass2_kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(buf_, grid.x * grid.y);
+        switch (cn)
+        {
+        case 1:
+            sum_kernel<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
+                    src, (typename TypeVec<R, 1>::vec_t*)buf.ptr(0));
+            sum_pass2_kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
+                    (typename TypeVec<R, 1>::vec_t*)buf.ptr(0), grid.x * grid.y);
+        case 2:
+            sum_kernel_C2<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
+                    src, (typename TypeVec<R, 2>::vec_t*)buf.ptr(0));
+            sum_pass2_kernel_C2<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
+                    (typename TypeVec<R, 2>::vec_t*)buf.ptr(0), grid.x * grid.y);
+        }
         cudaSafeCall(cudaThreadSynchronize());
 
-        R result = 0;
-        cudaSafeCall(cudaMemcpy(&result, buf_, sizeof(result), cudaMemcpyDeviceToHost));
-        sum[0] = result;
+        R result[4] = {0, 0, 0, 0};
+        cudaSafeCall(cudaMemcpy(&result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+
+        sum[0] = result[0];
+        sum[1] = result[1];
+        sum[2] = result[2];
+        sum[3] = result[3];
     }  
 
-    template void sum_multipass_caller<unsigned char>(const DevMem2D, PtrStep, double*);
-    template void sum_multipass_caller<char>(const DevMem2D, PtrStep, double*);
-    template void sum_multipass_caller<unsigned short>(const DevMem2D, PtrStep, double*);
-    template void sum_multipass_caller<short>(const DevMem2D, PtrStep, double*);
-    template void sum_multipass_caller<int>(const DevMem2D, PtrStep, double*);
-    template void sum_multipass_caller<float>(const DevMem2D, PtrStep, double*);
+    template void sum_multipass_caller<unsigned char>(const DevMem2D, PtrStep, double*, int);
+    template void sum_multipass_caller<char>(const DevMem2D, PtrStep, double*, int);
+    template void sum_multipass_caller<unsigned short>(const DevMem2D, PtrStep, double*, int);
+    template void sum_multipass_caller<short>(const DevMem2D, PtrStep, double*, int);
+    template void sum_multipass_caller<int>(const DevMem2D, PtrStep, double*, int);
+    template void sum_multipass_caller<float>(const DevMem2D, PtrStep, double*, int);
 
 
     template <typename T>
-    void sqsum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum)
+    void sum_caller(const DevMem2D src, PtrStep buf, double* sum, int cn)
     {
         using namespace sum;
         typedef typename SumType<T>::R R;
@@ -1581,27 +1700,38 @@ namespace cv { namespace gpu { namespace mathfunc
         estimate_thread_cfg(src.cols, src.rows, threads, grid);
         set_kernel_consts(src.cols, src.rows, threads, grid);
 
-        R* buf_ = (R*)buf.ptr(0);
-
-        sum_kernel<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>((const DevMem2D_<T>)src, buf_);
-        sum_pass2_kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(buf_, grid.x * grid.y);
+        switch (cn)
+        {
+        case 1:
+            sum_kernel<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
+                    src, (typename TypeVec<R, 1>::vec_t*)buf.ptr(0));
+            break;
+        case 2:
+            sum_kernel_C2<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
+                    src, (typename TypeVec<R, 2>::vec_t*)buf.ptr(0));
+            break;
+        }
         cudaSafeCall(cudaThreadSynchronize());
 
-        R result = 0;
-        cudaSafeCall(cudaMemcpy(&result, buf_, sizeof(result), cudaMemcpyDeviceToHost));
-        sum[0] = result;
+        R result[4] = {0, 0, 0, 0};
+        cudaSafeCall(cudaMemcpy(&result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+
+        sum[0] = result[0];
+        sum[1] = result[1];
+        sum[2] = result[2];
+        sum[3] = result[3];
     }  
 
-    template void sqsum_multipass_caller<unsigned char>(const DevMem2D, PtrStep, double*);
-    template void sqsum_multipass_caller<char>(const DevMem2D, PtrStep, double*);
-    template void sqsum_multipass_caller<unsigned short>(const DevMem2D, PtrStep, double*);
-    template void sqsum_multipass_caller<short>(const DevMem2D, PtrStep, double*);
-    template void sqsum_multipass_caller<int>(const DevMem2D, PtrStep, double*);
-    template void sqsum_multipass_caller<float>(const DevMem2D, PtrStep, double*);
+    template void sum_caller<unsigned char>(const DevMem2D, PtrStep, double*, int);
+    template void sum_caller<char>(const DevMem2D, PtrStep, double*, int);
+    template void sum_caller<unsigned short>(const DevMem2D, PtrStep, double*, int);
+    template void sum_caller<short>(const DevMem2D, PtrStep, double*, int);
+    template void sum_caller<int>(const DevMem2D, PtrStep, double*, int);
+    template void sum_caller<float>(const DevMem2D, PtrStep, double*, int);
 
 
     template <typename T>
-    void sum_caller(const DevMem2D src, PtrStep buf, double* sum)
+    void sqsum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum)
     {
         using namespace sum;
         typedef typename SumType<T>::R R;
@@ -1610,23 +1740,23 @@ namespace cv { namespace gpu { namespace mathfunc
         estimate_thread_cfg(src.cols, src.rows, threads, grid);
         set_kernel_consts(src.cols, src.rows, threads, grid);
 
-        R* buf_ = (R*)buf.ptr(0);
-
-        sum_kernel<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>((const DevMem2D_<T>)src, buf_);
+        sum_kernel<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
+                src, (typename TypeVec<R, 1>::vec_t*)buf.ptr(0));
+        sum_pass2_kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
+                (typename TypeVec<R, 1>::vec_t*)buf.ptr(0), grid.x * grid.y);
         cudaSafeCall(cudaThreadSynchronize());
 
         R result = 0;
-        cudaSafeCall(cudaMemcpy(&result, buf_, sizeof(result), cudaMemcpyDeviceToHost));
+        cudaSafeCall(cudaMemcpy(&result, buf.ptr(0), sizeof(R), cudaMemcpyDeviceToHost));
         sum[0] = result;
     }  
 
-    template void sum_caller<unsigned char>(const DevMem2D, PtrStep, double*);
-    template void sum_caller<char>(const DevMem2D, PtrStep, double*);
-    template void sum_caller<unsigned short>(const DevMem2D, PtrStep, double*);
-    template void sum_caller<short>(const DevMem2D, PtrStep, double*);
-    template void sum_caller<int>(const DevMem2D, PtrStep, double*);
-    template void sum_caller<float>(const DevMem2D, PtrStep, double*);
-    template void sum_caller<double>(const DevMem2D, PtrStep, double*);
+    template void sqsum_multipass_caller<unsigned char>(const DevMem2D, PtrStep, double*);
+    template void sqsum_multipass_caller<char>(const DevMem2D, PtrStep, double*);
+    template void sqsum_multipass_caller<unsigned short>(const DevMem2D, PtrStep, double*);
+    template void sqsum_multipass_caller<short>(const DevMem2D, PtrStep, double*);
+    template void sqsum_multipass_caller<int>(const DevMem2D, PtrStep, double*);
+    template void sqsum_multipass_caller<float>(const DevMem2D, PtrStep, double*);
 
 
     template <typename T>
@@ -1639,13 +1769,12 @@ namespace cv { namespace gpu { namespace mathfunc
         estimate_thread_cfg(src.cols, src.rows, threads, grid);
         set_kernel_consts(src.cols, src.rows, threads, grid);
 
-        R* buf_ = (R*)buf.ptr(0);
-
-        sum_kernel<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>((const DevMem2D_<T>)src, buf_);
+        sum_kernel<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
+                src, (typename TypeVec<R, 1>::vec_t*)buf.ptr(0));
         cudaSafeCall(cudaThreadSynchronize());
 
         R result = 0;
-        cudaSafeCall(cudaMemcpy(&result, buf_, sizeof(result), cudaMemcpyDeviceToHost));
+        cudaSafeCall(cudaMemcpy(&result, buf.ptr(0), sizeof(R), cudaMemcpyDeviceToHost));
         sum[0] = result;
     }  
 
@@ -1655,6 +1784,5 @@ namespace cv { namespace gpu { namespace mathfunc
     template void sqsum_caller<short>(const DevMem2D, PtrStep, double*);
     template void sqsum_caller<int>(const DevMem2D, PtrStep, double*);
     template void sqsum_caller<float>(const DevMem2D, PtrStep, double*);
-    template void sqsum_caller<double>(const DevMem2D, PtrStep, double*);
 }}}
 
diff --git a/modules/gpu/src/opencv2/gpu/device/vecmath.hpp b/modules/gpu/src/opencv2/gpu/device/vecmath.hpp
index 19e1e88c62..dc04203e5d 100644
--- a/modules/gpu/src/opencv2/gpu/device/vecmath.hpp
+++ b/modules/gpu/src/opencv2/gpu/device/vecmath.hpp
@@ -866,6 +866,91 @@ namespace cv
                 return make_float4(a.x * s, a.y * s, a.z * s, a.w * s);
             }
 
+
+            static __device__  uint1 operator+(const uint1& a, const uint1& b)
+            {
+                return make_uint1(a.x + b.x);
+            }
+            static __device__  uint1 operator-(const uint1& a, const uint1& b)
+            {
+                return make_uint1(a.x - b.x);
+            }
+            static __device__  uint1 operator*(const uint1& a, const uint1& b)
+            {
+                return make_uint1(a.x * b.x);
+            }
+            static __device__  uint1 operator/(const uint1& a, const uint1& b)
+            {
+                return make_uint1(a.x / b.x);
+            }
+            static __device__ float1 operator*(const uint1& a, float s)
+            {
+                return make_float1(a.x * s);
+            }
+
+            static __device__  uint2 operator+(const uint2& a, const uint2& b)
+            {
+                return make_uint2(a.x + b.x, a.y + b.y);
+            }
+            static __device__  uint2 operator-(const uint2& a, const uint2& b)
+            {
+                return make_uint2(a.x - b.x, a.y - b.y);
+            }
+            static __device__  uint2 operator*(const uint2& a, const uint2& b)
+            {
+                return make_uint2(a.x * b.x, a.y * b.y);
+            }
+            static __device__  uint2 operator/(const uint2& a, const uint2& b)
+            {
+                return make_uint2(a.x / b.x, a.y / b.y);
+            }
+            static __device__ float2 operator*(const uint2& a, float s)
+            {
+                return make_float2(a.x * s, a.y * s);
+            }
+
+            static __device__  uint3 operator+(const uint3& a, const uint3& b)
+            {
+                return make_uint3(a.x + b.x, a.y + b.y, a.z + b.z);
+            }
+            static __device__  uint3 operator-(const uint3& a, const uint3& b)
+            {
+                return make_uint3(a.x - b.x, a.y - b.y, a.z - b.z);
+            }
+            static __device__  uint3 operator*(const uint3& a, const uint3& b)
+            {
+                return make_uint3(a.x * b.x, a.y * b.y, a.z * b.z);
+            }
+            static __device__  uint3 operator/(const uint3& a, const uint3& b)
+            {
+                return make_uint3(a.x / b.x, a.y / b.y, a.z / b.z);
+            }
+            static __device__ float3 operator*(const uint3& a, float s)
+            {
+                return make_float3(a.x * s, a.y * s, a.z * s);
+            }
+
+            static __device__  uint4 operator+(const uint4& a, const uint4& b)
+            {
+                return make_uint4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w);
+            }
+            static __device__  uint4 operator-(const uint4& a, const uint4& b)
+            {
+                return make_uint4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w);
+            }
+            static __device__  uint4 operator*(const uint4& a, const uint4& b)
+            {
+                return make_uint4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w);
+            }
+            static __device__  uint4 operator/(const uint4& a, const uint4& b)
+            {
+                return make_uint4(a.x / b.x, a.y / b.y, a.z / b.z, a.w / b.w);
+            }
+            static __device__ float4 operator*(const uint4& a, float s)
+            {
+                return make_float4(a.x * s, a.y * s, a.z * s, a.w * s);
+            }
+
             static __device__  float1 operator+(const float1& a, const float1& b)
             {
                 return make_float1(a.x + b.x);
diff --git a/tests/gpu/src/arithm.cpp b/tests/gpu/src/arithm.cpp
index 944ec096d0..f20c03adea 100644
--- a/tests/gpu/src/arithm.cpp
+++ b/tests/gpu/src/arithm.cpp
@@ -942,9 +942,18 @@ struct CV_GpuSumTest: CvTest
             Scalar a, b;
             double max_err = 1e-5;
 
-            int typemax = hasNativeDoubleSupport(getDevice()) ? CV_64F : CV_32F;
+            int typemax = CV_32F;
             for (int type = CV_8U; type <= typemax; ++type) 
             {
+                gen(1 + rand() % 500, 1 + rand() % 500, CV_MAKETYPE(type, 2), src);
+                a = sum(src);
+                b = sum(GpuMat(src));
+                if (abs(a[0] - b[0]) + abs(a[1] - b[1]) > src.size().area() * max_err)
+                {
+                    ts->printf(CvTS::CONSOLE, "cols: %d, rows: %d, expected: %f, actual: %f\n", src.cols, src.rows, a[0], b[0]);
+                    ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
+                    return;
+                }
                 gen(1 + rand() % 500, 1 + rand() % 500, type, src);
                 a = sum(src);
                 b = sum(GpuMat(src));