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Blocking sparse method for image denoising

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Abstract

In recent years, compressive sensing has been one promising technique for denoising images. This paper presents a new denoising model based on blocking sparsity. First, an image is blocked. Second, the split-Bregman method is used to solve for each block image. Finally, all denoised block images are combined into one image. Compared with the latest HTV, GHNS, FastATV, CSR and BM3D models, experimental results demonstrate that the proposed method is efficient, and has better denoising capability.

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Acknowledgements

I appreciates my graduate student, Jiao He. She makes some contributions for the revision of the manuscript. This work is supported by Fundamental Research Funds for the Central Universities (No. XDJK2020B033).

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Authors and Affiliations

  1. College of Artificial Intelligence, Southwest University, Chongqing, 400715, China

    Jianjun Yuan & Jiao He

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  1. Jianjun Yuan
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  2. Jiao He
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Correspondence to Jianjun Yuan.

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Yuan, J., He, J. Blocking sparse method for image denoising. Pattern Anal Applic 24, 1125–1133 (2021). https://doi.org/10.1007/s10044-021-00974-0

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  • DOI: https://doi.org/10.1007/s10044-021-00974-0

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