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Sparse representation with enhanced nonlocal self-similarity for image denoising

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Abstract

In the past decade, the sparsity prior of image is investigated and utilized widely as the development of compressed sensing theory. The dictionary learning combined with the convex optimization methods promotes the sparse representation to be one of the state-of-the-art techniques in image processing, such as denoising, super-resolution, deblurring, and inpainting. Empirically, the sparser of image representation, the better of image restoration. In this work, the non-local clustering sparse representation is applied with optimized matching strategies of self-similar patches, which break through the bottleneck of search window (localization) and improve the estimation effect of the sparse coefficient. The experimental results show that the proposed method provides an effective suppression on noise, preserves more details of image and presents more comfortable visual experience.

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Authors and Affiliations

  1. Shanghai Integrated Circuits R&D Center Co., Ltd, Shanghai, 201203, China

    Tao Zhou, Chen Li & Yuhang Zhao

  2. State Key Lab of ASIC & System, School of Microelectronics, Fudan University, Shanghai, 201203, China

    Xuan Zeng

Authors
  1. Tao Zhou
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  2. Chen Li
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  3. Xuan Zeng
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  4. Yuhang Zhao
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Correspondence to Chen Li.

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Zhou, T., Li, C., Zeng, X. et al. Sparse representation with enhanced nonlocal self-similarity for image denoising. Machine Vision and Applications 32, 110 (2021). https://doi.org/10.1007/s00138-021-01232-3

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  • DOI: https://doi.org/10.1007/s00138-021-01232-3

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