Wavelet sparse transform optimization in image reconstruction based on compressed sensing

CORC > 光电技术研究所 > 中国科学院光电技术研究所 > 光电探测与信号处理研究室（五室）

	Wavelet sparse transform optimization in image reconstruction based on compressed sensing
	Ziran, Wei 1,2; Huachuang, Wang 1; Jianlin, Zhang 1
	2017
关键词	Compressed sensing - Image processing - Image reconstruction - Signal reconstruction - Signal to noise ratio - Wavelet transforms
卷号	69
期号	1
页码	012165
英文摘要	The high image sparsity is very important to improve the accuracy of compressed sensing reconstruction image, and the wavelet transform can make the image sparse obviously. This paper is the optimization method based on wavelet sparse transform in image reconstruction based on compressed sensing, and we have designed a restraining matrix to optimize the wavelet sparse transform. Firstly, the wavelet coefficients are obtained by wavelet transform of the original signal data, and the wavelet coefficients have a tendency of decreasing gradually. The restraining matrix is used to restrain the small coefficients and is a part of image sparse transform, so as to make the wavelet coefficients more sparse. When the sampling rate is between 0. 15 and 0. 45, the simulation results show that the quality promotion of the reconstructed image is the best, and the peak signal to noise ratio (PSNR) is increased by about 0.5dB to 1dB. At the same time, it is more obvious to improve the reconstruction accuracy of the fingerprint texture image, which to some extent makes up for the shortcomings that reconstruction of texture image by compressed sensing based on the wavelet transform has the low accuracy. © Published under licence by IOP Publishing Ltd.
会议录	1755-1307
语种	英语
内容类型	会议论文
源URL	[http://ir.ioe.ac.cn/handle/181551/9014]
专题	光电技术研究所_光电探测与信号处理研究室（五室）
作者单位	1.Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu; 610209, China; 2.University of Chinese Academy of Sciences, Beijing; 100039, China
推荐引用方式 GB/T 7714	Ziran, Wei,Huachuang, Wang,Jianlin, Zhang. Wavelet sparse transform optimization in image reconstruction based on compressed sensing[C]. 见:.