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Parallel transformation of K-SVD solar image denoising algorithm
Liang, Youwen1,2,3; Tian, Yu1,2; Li, Mei1,2
2017
会议名称Proceedings of SPIE: Second International Conference on Photonics and Optical Engineering
会议录名称0277-786X
卷号10256
页码1025614
摘要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. © 2017 SPIE.
关键词Application programming interfaces (API) - Parallel processing systems - Parallel programming - Photonics - Signal to noise ratio - White noise
收录类别SCI ; Ei
语种英语
EI分类号2017-2168
文献类型会议论文
条目标识符http://ir.ioe.ac.cn/handle/181551/9036
专题自适应光学技术研究室(八室)
作者单位1.Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu; 610209, China;
2.Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu; 610209, China;
3.University of Chinese, Academy of Sciences, Beijing; 100049, China
推荐引用方式
GB/T 7714
Liang, Youwen,Tian, Yu,Li, Mei. Parallel transformation of K-SVD solar image denoising algorithm[C],2017:1025614.
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