4 Replies Latest reply on May 9, 2012 10:34 PM by Wenju

    Gaussian blur



      I'm trying to do a gaussian blur on an image, but all the algorithms I found are for separable gaussian (the blur is done horizontally then vertically), so it's 2 1-dimensionnal operations.

      I'm looking for how to prform a single pass 2-dimansionnal gaussian blur.



        • Gaussian blur

          that is bad idea. because with N wide convolution you got O(N^2) against O(2*N) for two pass.

          but gausian blur is simple. just get N*N of input image multiple with convolution matrix and then sum up. thats all. but it is slow for anything larger thatn 3x3.

            • Gaussian blur

              I did it in two passes, but now I have to implement it in a 2-dimension kernel, wich does a transformation on the image before gaussian blur, so even though it's slower, but I think it's the best suited to my application.

              I tried image convolution with gauss coeficient but doesn't work,

              is someone got the gaussian convolution matrixes, how the compute is done?


                • Re: Gaussian blur



                  In one of my projects a user-configurable general convolution with kernels of up to 9x9 entries is used.

                  I use a 5x5 blur kernel and the performance impact isn't that big on a 4850. Comparing that to a simmilar 3x3 kernel, the difference is marginal.

                  Once you set a maximum kernel size, the shader (in my case GLSL) is quite straight forward to implement.


                  So the short answer is: yes, it is possible.


                  Here's a very simple GLSL fragment shader (I bet it can be optimized a lot by someone more experienced with GLSL than me):


                  #version 130
                  in vec4 Color;
                  in vec2 TextureCoordinate;
                  const int c_iMaxKernelSize = 9 * 9;
                  uniform int u_iKernelSize;
                  uniform float u_fWeights[c_iMaxKernelSize];
                  uniform vec2 u_v2Offsets[c_iMaxKernelSize];
                  uniform sampler2D u_s2ScreenImage;
                  out vec4 FragColor;
                  void main()
                       vec4 ColorSum = vec4(0.0);
                       for(int i = 0; i < u_iKernelSize; i++)
                            vec4 ColorSample = Color * texture2D(u_s2ScreenImage, TextureCoordinate + u_v2Offsets[ i ]);
                            ColorSum += ColorSample * u_fWeights[ i ];
                       FragColor = vec4(ColorSum.rgb, 1.0);


                  The shader needs the current kernel's size (number of entries in the array) in u_iKernelSize, an array with the kernel's weights (the kernel) in u_fWeights[] and an array of texel offsets (entry's uv distances from the center - the center is always {0.0, 0.0}) in u_v2Offsets[] to apply the convolution to the image bound to the sampler u_s2ScreenImage.

                  Keep in mind, that all of these parameters (uniforms) can be set only once after the kernel is available. This might be during program initialization or after loading a kernel configuration.

                  The corresponding vertex shader can be very simple and just has to pass a color and texture coordiantes in addition to the transformed vertex position.

                  I hope this helps.


                  Best regards,



                  PS: You can use the code freely (no warranties and no liability, though ). I posted it under the WTFPL version 2.


                  EDIT: As the color is not used here, you might remove it.

                  • Re: Gaussian blur



                    If you want to perform 2D convolution in each thead, it would be a good idea to make use of local memory.


                    To convolve with 2D Gaussian mask, you have to:

                    1) flip the mask in both horizontal and vertical direction,

                    2) move mask center to the sample we are computing,

                    3) multiply each mask element with its overlapped sample,

                    4) sum.