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Parallel transformation of K-SVD solar image denoising algorithm
Author(s): Youwen Liang; Yu Tian; Mei Li
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Paper 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.

Paper Details

Date Published: 28 February 2017
PDF: 7 pages
Proc. SPIE 10256, Second International Conference on Photonics and Optical Engineering, 1025614 (28 February 2017); doi: 10.1117/12.2256495
Show Author Affiliations
Youwen Liang, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Yu Tian, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics (China)
Mei Li, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 10256:
Second International Conference on Photonics and Optical Engineering
Chunmin Zhang; Anand Asundi, Editor(s)

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