28 February 2017 Parallel transformation of K-SVD solar image denoising algorithm
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Proceedings Volume 10256, Second International Conference on Photonics and Optical Engineering; 1025614 (2017) https://doi.org/10.1117/12.2256495
Event: Second International Conference on Photonics and Optical Engineering, 2016, Xi'an, China
Abstract
The images obtained by observing the sun through a large telescope always suffered with noise due to the low SNR. K-SVD denoising algorithm can effectively remove Gauss white noise. Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. In this paper, an OpenMP parallel programming language is proposed to transform the serial algorithm to the parallel version. Data parallelism model is used to transform the algorithm. Not one atom but multiple atoms updated simultaneously is the biggest change. The denoising effect and acceleration performance are tested after completion of the parallel algorithm. Speedup of the program is 13.563 in condition of using 16 cores. This parallel version can fully utilize the multi-core CPU hardware resources, greatly reduce running time and easily to transplant in multi-core platform.
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Youwen Liang, Yu Tian, Mei Li, "Parallel transformation of K-SVD solar image denoising algorithm", Proc. SPIE 10256, Second International Conference on Photonics and Optical Engineering, 1025614 (28 February 2017); doi: 10.1117/12.2256495; https://doi.org/10.1117/12.2256495
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