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

Zhao, Hui-Huang, Rosin, Paul, Lai, Yukun, Zheng, Jin-Hua and Wang, Yao-Nan 2019. Adaptive gradient-based block compressive sensing with sparsity for noisy images. Multimedia Tools and Applications 10.1007/s11042-019-7647-8
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Abstract

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 better results by using the sparsity of pixels to adaptively select block shape. Experimental results with different image sets demonstrate that our AGbBCS_SP method is able to achieve better performance, in terms of peak signal to noise ratio (PSNR) and computational cost, than several classical algorithms.

Item Type: Article
Date Type: Publication
Status: In Press
Schools: Computer Science & Informatics
Publisher: Springer
ISSN: 1380-7501
Date of First Compliant Deposit: 7 May 2019
Date of Acceptance: 15 April 2019
Last Modified: 19 Oct 2019 13:37
URI: http://orca-mwe.cf.ac.uk/id/eprint/122189

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