A GPU Accelerated Algorithm for Compressive Sensing Based Image Super-Resolution

this paper presents a parallel algorithm designed for Super-resolution Image Reconstruction based on Compressive sensing in the ATI Stream platform. In the accelerating process, we select part of the serial program as the objects to be sped up according to the execution time of each stage, set appropriate parallel granularity to make full use of GPU's computational horsepower, and make a rational use of different kinds of memory space in GPU. At last, the result of the parallel algorithm is shown and analyzed. Compared to the serial algorithm, parallel algorithm has significantly accelerated results.

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