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Adaptive Block Compressive Sensing for Noisy Images

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Book cover Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

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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. 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.

Author information

Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hunan, China

    Hui-huang Zhao & Jing-hua Zheng

  2. College of Computer Science and Technology, Hengyang Normal University, Hengyang, China

    Hui-huang Zhao & Jing-hua Zheng

  3. School of Computer Science and Informatics, Cardiff University, Cardiff, UK

    Paul L. Rosin & Yu-Kun Lai

  4. College of Electrical and Information Engineering, Hunan University, Changsha, China

    Yao-nan Wang

Authors
  1. Hui-huang Zhao
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  2. Paul L. Rosin
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  3. Yu-Kun Lai
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  4. Jing-hua Zheng
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  5. Yao-nan Wang
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Corresponding author

Correspondence to Hui-huang Zhao .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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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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