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Wavelet sparse transform optimization in image reconstruction based on compressed sensing
Ziran, Wei1,2; Huachuang, Wang1; Jianlin, Zhang1
Source Publication1755-1307
Indexed ByEi
AbstractThe 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.
KeywordCompressed sensing - Image processing - Image reconstruction - Signal reconstruction - Signal to noise ratio - Wavelet transforms
Conference NameIOP Conference Series: 3rd International Conference on Advances in Energy, Environment and Chemical Engineering
EI Classification Number2017-2146
Document Type会议论文
Affiliation1.Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu; 610209, China;
2.University of Chinese Academy of Sciences, Beijing; 100039, China
Recommended Citation
GB/T 7714
Ziran, Wei,Huachuang, Wang,Jianlin, Zhang. Wavelet sparse transform optimization in image reconstruction based on compressed sensing[C],2017:012165.
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