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Adaptive gradient-based block compressive sensing with sparsity for noisy images

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成果类型:
期刊论文
作者:
Zhao, Hui-Huang*;Rosin, Paul L.;Lai, Yu-Kun;Zheng, Jin-Hua;Wang, Yao-Nan
通讯作者:
Zhao, Hui-Huang
作者机构:
[Zhao, Hui-Huang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang, Hunan, Peoples R China.
[Zheng, Jin-Hua; Zhao, Hui-Huang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
[Lai, Yu-Kun; Rosin, Paul L.] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.
[Wang, Yao-Nan] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.
通讯机构:
[Zhao, Hui-Huang] H
Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang, Hunan, Peoples R China.
Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
语种:
英文
关键词:
Block Compressive Sensing (CS);Adaptive;Convex optimization;Sparsity
期刊:
Multimedia Tools and Applications
ISSN:
1380-7501
年:
2020
卷:
79
期:
21-22
页码:
14825-14847
机构署名:
本校为通讯机构
院系归属:
计算机科学与技术学院
摘要:
This paper develops a novel adaptive gradient-based block compressive sensing (AGbBCS_SP) methodology for noisy image compression and reconstruction. The AGbBCS_SP approach splits an image into blocks by maximizing their sparsity, and reconstructs images by solving a convex optimization problem. In block compressive sensing, the commonly used square block shapes cannot always produce the best results. The main contribution of our paper is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. The proposed algorithm can adaptively achieve bett...

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