中文摘要：

利用图像的非局部相似性先验以提升图像恢复质量已得到广泛关注。为了更有效地提升压缩感知（CS）图像的重构质量,提出了一种基于加权结构组稀疏表示（WSGSR）的图像压缩感知重构方法。采用非局部相似图像块结构组加权稀疏表示的1l范数作为规则化项约束优化重构,实现在更好地恢复图像高频细节信息的同时有效减少对图像低频成分的损失,图像重构质量得到明显改善。推导出一种加权软阈值收缩方法,实现对模型的优化求解,对幅值较大的重要系数采用较小的阈值收缩处理,对幅值较小的非重要系数采用相对较大的阈值收缩处理。实验结果比较验证了所提方法的有效性。

英文摘要：

Non-local similarity prior has been widely paid attention to efficiently improve image recovery quality.To further 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.1l-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 reconstructed 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 demonstrate the effectiveness of the proposed method.