Image compressive sensing recovery based on weighted structure group sparse representation - Jia LI; Zhi-rong GAO; Cheng-yi XIONG; Cheng ZHOU (Tongxin xuebao)
Non-local similarity prior has been widely paid attention to efficiently improve image recovery quality.To fur-ther improve the recovered image quality for compressive sensing (CS),an image compressive sensing recovery method based on reweighted structure group sparse representation (WSGSR) was proposed. l 1 -norm of WSGSR of image non-local similar patch group was used as a regularization term to optimize reconstruction,which achieved well reserving image high-frequency detail with less loss of image low-frequency component,and thus considerably improve the recon-structed image quality.A reweighted soft thresholding shrinkage method was deduced to achieve optimization solution,in which the significant coefficient with large magnitude value was shrunk by a small threshold,while the non-significant coefficient with small magnitude value was shrunk by a relative large threshold.Experimental results comparison demon-strate the effectiveness of the proposed method.