Parallel transformation of K-SVD solar image denoising algorithm

CORC > 光电技术研究所 > 中国科学院光电技术研究所 > 自适应光学技术研究室（八室）

	Parallel transformation of K-SVD solar image denoising algorithm
	Liang, Youwen 1,2,3; Tian, Yu 1,2; Li, Mei 1,2
	2017
关键词	Application programming interfaces (API) - Parallel processing systems - Parallel programming - Photonics - Signal to noise ratio - White noise
卷号	10256
页码	1025614
英文摘要	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.
会议录	0277-786X
语种	英语
内容类型	会议论文
源URL	[http://ir.ioe.ac.cn/handle/181551/9036]
专题	光电技术研究所_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]. 见:.