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GPU Computing: Image Convolution

Jan Novak, Anton Kaplanyan, Gabor Liktor, Carsten Dachsbacher
Karlsruhe Institute of Technology
Karlsruhe Institute of Technology, 2012

@article{novak2012gpu,

   title={GPU Computing: Image Convolution},

   author={Novak, Jan and Kaplanyan, Anton and Liktor, Gabor and Dachsbacher, Carsten},

   year={2012}

}

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Convolution of two functions is an important mathematical operation that found heavy application in signal processing. In computer graphics and image processing we usually work with discrete functions (e.g. an image) and apply a discrete form of the convolution to remove high frequency noise, sharpen details, detect edges, or otherwise modulate the frequency domain of the image. In this assignment, we discuss an efficient implementation of image convolution filters on the GPU. A general 2D convolution has a high bandwidth requirement as the final value of a given pixel is determined by several neighboring pixels. Since memory bandwidth is usually the main limiting factor of algorithm’s performance, our optimization techniques will focus on minimizing global memory accesses during the computations.
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