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. The main contribution is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. Experimental results with different image sets indicate 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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