自动化学报Issue(2)：261-272,12.DOI:10.16383/j.aas.2015.c140210
基于非局部相似模型的压缩感知图像恢复算法
Image Reconstruction Algorithm of Compressed Sensing Based on Nonlocal Similarity Model
摘要
Abstract
In this paper, an image reconstruction algorithm of compressed sensing (CS) is proposed based on nonlocal similarity model. Instead of using the traditional sparse property of 2D image blocks, the sparse representation of 3D similar image block group is exploited to increase the sparse degree of reconstructed image and improve the performance of the compressed sensing reconstruction algorithm. The texture and structure features are well preserved in the recon-structed image. In the solution of our proposed algorithm, the constrained optimization problem is transformed into an unconstrained optimization problem by augmented Lagrangian method, and the linear technique, which is based on Taylor expansion, is employed to reduce the computational burden and accelerates our proposed algorithm. Experimental results show that the subjective and objective performance of our proposed reconstruction algorithm is superior to the state of art reconstruction algorithms.关键词
压缩感知/图像恢复/非局部相似/稀疏表示Key words
Compressed sensing (CS)/image reconstruction/nonlocal similarity/sparse representation引用本文复制引用
沈燕飞,李锦涛,朱珍民,张勇东,代锋..基于非局部相似模型的压缩感知图像恢复算法[J].自动化学报,2015,(2):261-272,12.基金项目
国家自然科学基金(61327013,61471343),中国科学院科研装备研制项目(YZ201321)资助@@@@Supported by National Natural Science Foundation of China (61327013,61471343) and Instrument Developing Project of the Chinese Academy of Sciences (YZ201321) （61327013,61471343）
