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Image restoration using spatially variant hyper-Laplacian prior

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Abstract

The heavy-tailed hyper-Laplacian prior has been successfully applied in image restoration tasks, in which the unified distribution is adopted for the whole image. However, the gradient distribution of natural image is reasonably assumed to be spatially variant, e.g., gradient distribution of the region with less texture is more heavy-tailed. In this paper, we propose to model the gradient distribution of natural images as spatially variant hyper-Laplacian. The proposed model adapts the hyper-Laplacian parameters to each pixel. Within the maximum a posterior model, we address the problem using an alternating optimization method. Also the proposed model is free from tedious tuning trade-off parameters. Compared with the deconvolution algorithm using uniform hyper-Laplacian prior, the experimental results validate that the proposed model can achieve better restoration results in terms of visual quality and quantitative indicators.

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Authors and Affiliations

  1. Heilongjiang University of Science and Technology, School of Mechanical Engineering, Puyuan Road No. 2468, Harbin, 150022, China

    Junting Cheng, Yi Gao & Boyang Guo

  2. Harbin Institute of Technology, School of Computer Science and Technology, Xidazhi Street No. 90, Harbin, 150001, China

    Wangmeng Zuo

Authors
  1. Junting Cheng
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  2. Yi Gao
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  3. Boyang Guo
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  4. Wangmeng Zuo
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Correspondence to Yi Gao.

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Cheng, J., Gao, Y., Guo, B. et al. Image restoration using spatially variant hyper-Laplacian prior. SIViP 13, 155–162 (2019). https://doi.org/10.1007/s11760-018-1340-7

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  • DOI: https://doi.org/10.1007/s11760-018-1340-7

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