电子学报2017，Vol.45Issue(3)：695-703,9.DOI:10.3969/j.issn.0372-2112.2017.03.029
基于非局部相似块低秩的压缩感知图像重建算法
Compressed Sensing Image Reconstruction Based on Low Rank of Non-local Similar Patches
摘要
Abstract
Generally,traditional compressed sensing (CS) image recovery methods build the objective optimization function by using the signal sparsity in some specific feature spaces.They do not fully take the local features and structural properties of signal into account,which leads to constraints of the recovery performance and flexibility.In this paper,considering the non-local self-similarity (NLSS) in images,we propose an image CS reconstruction method based on the image low-rank property by converting the CS recovery problem into a matrix rank minimization problem of aggregating similar image patches.The proposed algorithm builds optimization model under the constraint of minimal recovery errors and employs the weighed nuclear norm minimization (WNNM) method to solve the low-rank optimization problem.By taking advantage of them,the proposed method exploits the self-information and structured sparse characteristics of the image very well,and therefore provides a better protection of image structures and textures.Experiments on different test images under various sampling rates have shown the effectiveness of the proposed algorithm.Especially,for richly-textured images,our method outperforms the art-of-the-state algorithms significantly under low sampling rates.关键词
压缩感知/图像重建/非局部白相似/低秩优化Key words
compressive sensing/image recovery/non-local self-similarity/low-rank optimization分类
信息技术与安全科学引用本文复制引用
宋云,李雪玉,沈燕飞,杨高波..基于非局部相似块低秩的压缩感知图像重建算法[J].电子学报,2017,45(3):695-703,9.基金项目
国家自然科学基金(No.61471343,No.61572183,No.61402053) （No.61471343,No.61572183,No.61402053）
湖南省教育厅科学研究重点项目(No.13A107,No.15A007) （No.13A107,No.15A007）
湖南省自然科学基金(No.2016JJ2005) （No.2016JJ2005）
湖南省科技计划项目(No.2014FJ6047,No.2014GK3030) （No.2014FJ6047,No.2014GK3030）
