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Hybrid priors based on weighted hyper-Laplacian with overlapping group sparsity for poisson noise removal

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Abstract

Poisson noise widely exists in photo-limited imaging systems, which is very difficult to remove because of its signal-dependent and multiplicative characteristics. In this paper, we propose a new hybrid regularizer variational model for removing Poisson noise. Based on the weighted hyper-Laplacian prior, the hybrid model combines the overlapping group sparse total variation with the high-order nonconvex total variation (HONTV) as a hybrid regularizer. The proposed model combines the advantages of the HONTV regularizer and the weighted hyper-Laplacian prior with overlapping group sparsity regularizer, it can more effectively preserve sharp edges and details while alleviating the staircase artifacts. To solve the non-convex and non-smooth model, we proposed an efficient alternating minimization method under the framework of alternating direction method of multipliers, where the majorization-minimization algorithm and generalized soft threshold algorithm are adopted to solve the corresponding subproblems. Numerical experiments show that the proposed method has higher quality image recovery than several existing methods.

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Acknowledgements

This work was supported by a Project of Shandong Province Higher Educational Science and Technology Program (J17KA166), by the Joint Funds of the National Natural Science Foundation of China (U22B2049).

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Authors and Affiliations

  1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China

    Yuanna He & Jianguang Zhu

  2. College of Science, China University of Petroleum, Qingdao, 266580, China

    Binbin Hao

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  1. Yuanna He
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  2. Jianguang Zhu
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Correspondence to Jianguang Zhu.

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He, Y., Zhu, J. & Hao, B. Hybrid priors based on weighted hyper-Laplacian with overlapping group sparsity for poisson noise removal. SIViP 17, 2607–2615 (2023). https://doi.org/10.1007/s11760-022-02477-9

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  • DOI: https://doi.org/10.1007/s11760-022-02477-9

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