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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Acknowledgements
This work was supported by National Natural Science Foundation of China (61733004, 61503128, 61602402), the Science and Technology Plan Project of Hunan Province (2016TP102), Scientific Research Fund of Hunan Provincial Education Department (16C0226), and Hunan Provincial Natural Science Foundation (2017JJ4001). We would like to thank NVIDIA for the GPU donation.
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Zhao, Hh., Rosin, P.L., Lai, YK., Zheng, Jh., Wang, Yn. (2020). Adaptive Block Compressive Sensing for Noisy Images. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_38
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DOI: https://doi.org/10.1007/978-3-030-04946-1_38
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