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Adaptive compressive sensing of images using error between blocks

Author

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  • Ran Li
  • Xiaomeng Duan
  • Yongfeng Lv

Abstract

Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system.

Suggested Citation

  • Ran Li & Xiaomeng Duan & Yongfeng Lv, 2018. "Adaptive compressive sensing of images using error between blocks," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:6:p:1550147718781751
    DOI: 10.1177/1550147718781751
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    References listed on IDEAS

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    1. Marsaglia, George & Tsang, Wai Wan, 2000. "The Ziggurat Method for Generating Random Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i08).
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    Cited by:

    1. Dibyalekha Nayak & Kananbala Ray & Tejaswini Kar & Sachi Nandan Mohanty, 2023. "Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application," Mathematics, MDPI, vol. 11(7), pages 1-21, March.

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