Adaptive Block Compressive Sensing for Noisy Images

  • Hui-huang ZhaoEmail author
  • Paul L. Rosin
  • Yu-Kun Lai
  • Jing-hua Zheng
  • Yao-nan Wang
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


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.


Block Compressive Sensing (CS) Adaptive Convex Optimization Sparsity 



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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hui-huang Zhao
    • 1
    • 2
    Email author
  • Paul L. Rosin
    • 3
  • Yu-Kun Lai
    • 3
  • Jing-hua Zheng
    • 1
    • 2
  • Yao-nan Wang
    • 4
  1. 1.Hunan Provincial Key Laboratory of Intelligent Information Processing and ApplicationHunanChina
  2. 2.College of Computer Science and TechnologyHengyang Normal UniversityHengyangChina
  3. 3.School of Computer Science and InformaticsCardiff UniversityCardiffUK
  4. 4.College of Electrical and Information EngineeringHunan UniversityChangshaChina

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