Adaptive gradient-based block compressive sensing with sparsity for noisy images |
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Authors: | Zhao Hui-Huang Rosin Paul L. Lai Yu-Kun Zheng Jin-Hua Wang Yao-Nan |
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Affiliation: | 1.Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hunan, China ;2.College of Computer Science and Technology, Hengyang Normal University, Hengyang, China ;3.School of Computer Science and Informatics, Cardiff University, Cardiff, UK ;4.College of Electrical and Information Engineering, Hunan University, Changsha, China ; |
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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. |
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