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Low-rank decomposition on transformed feature maps domain for image denoising

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Abstract

Low-rank based models are proved outstanding for denoising on the data with strong repetitive or redundant property. However, for natural images with complex structures or rich details, the performance drops down because of the weak low-rankness of the data. A feasible solution is to transform the data into a suitable domain to further explore the underlying low-rank information. In this paper, we present a novel approach to create such a domain via a fully replicated linear autoencoder network. By applying various low-rank models to the feature maps generated by the encoder rather than the original data, and then performing inverse transformation by the decoder, their denoising performances all get enhanced. In addition, feature maps also show good sparsity, hence we introduce a new measure combining sparse and low-rank regularity, and further propose corresponding single image denoising model. Extensive experiments show the superiority of our work.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61773367, Grant 61303168, and Grant 61821005, in part by the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant 2016183.

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Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

    Qiong Luo, Baichen Liu, Zhi Han & Yandong Tang

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China

    Qiong Luo, Baichen Liu, Zhi Han & Yandong Tang

  3. University of Chinese Academy of Sciences, Beijing, China

    Qiong Luo & Baichen Liu

  4. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hongkong, China

    Yang Zhang

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  1. Qiong Luo
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  2. Baichen Liu
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  4. Zhi Han
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  5. Yandong Tang
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Correspondence to Zhi Han.

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Luo, Q., Liu, B., Zhang, Y. et al. Low-rank decomposition on transformed feature maps domain for image denoising. Vis Comput 37, 1899–1915 (2021). https://doi.org/10.1007/s00371-020-01951-0

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