I\'ve written this code to perform the 1-d convolution of a 2-d matrix valued function (k is my time index, kend is on the order of 10e3). Is there a faster or cleaner way to do this, perhaps usi开发者
I\'m looking at the CUDA SDK convolution with separable kernels, and I have a simple question but can\'t find an answer:
I\'m looking at the FFT example on the CUDA SDK and I\'m wondering: why the CUFFT is much faster when the half of the padded data is a power of two? (half because in frequency domain half i开发者_Stac
I need to implement an efficient version of an image convolution with non-separable kernels (so CUDA\'s sdk is useful just for the FFT example, but it is clearly stated that it works great only for bi
I tried to do convolution in R directly and using FFTs then taking inverse. But it seems from simple observation it is not correct. Look at this example:
I\'m very new to GLSL, but I\'m trying to write convolution kernel with in a fragment shader for image processing.I was able to do this just fine when my kernel was small (3x3) using a constant matrix
I have two 2-D arrays with the same first axis dimensions. In python, I would like to convolve the two matrices along the second axis only. I would like to get C below without computing the convolutio
i\'m trying to apply a box blur to an transparent image, and i\'m getting a \"dark halo\" around the edges.
I am designing a fractional delay filter, and my lagrange coefficient of order 5 h(n) have 6 taps in time domain. I have tested to convolute the h(n) with x(n) which is 5000 sampled signal using matla
While using MATLAB 2D filter funcion filter2(B,X) and convolution function conv(X,B,\'\'), I see that the filter2 function is essentially 2D convolution but with a rotation by 180 degrees of the filte
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