Low-Rank Enhancement-Based Compressed Image Sensing Reconstruction Algorithm

被引：0
|
作者：
Yang C. [1 ]
Tang R. [1 ]
机构：
[1] School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, Guangdong
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2018年 / 46卷 / 10期
关键词：
Compressed image sensing; Hybrid filtering; Key features; Low sampling rates; Low-rank enhancement;
D O I：
10.3969/j.issn.1000-565X.2018.10.010
中图分类号：
学科分类号：
摘要：
At low sampling rates, compressed image sensing algorithms based on nonlocal self-similarity of natural images suffer initial reconstruction with poor quality, leading to inappropriate grouping situations and an unsatisfying reconstruction result. To solve this problem, a low-rank enhancement reconstruction algorithm based on GSR is proposed. Firstly, a hybrid filtering reconstruction method is proposed in initial reconstruction that BM3D filtering is introduced to attain a better initially reconstructed result. Then a low-rank enhancement pretreatment is conducted before similar-block grouping to help grouping operation focus more on the key features. Simulation results indicate that the proposed algorithm, compared with GSR, possesses a better reconstruction performance at low sampling rates. © 2018, Editorial Department, Journal of South China University of Technology. All right reserved.
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收藏
页码：72 / 80
页数：8
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