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Image denoising via structure-constrained low-rank approximation


Low-rank approximation-based methods have recently achieved impressive results in image restoration. Generally, the low-rank constraint integrated with the nonlocal self-similarity prior is enforced for image recovery. However, it is still unsatisfactory to recover complex image structures due to the lack of joint modeling based on local and global information, especially when the signal-to-noise ratio is low. In this paper, we propose a novel structure-constrained low-rank approximation method using complementary local and global information, as, respectively, modeled by kernel Wiener filtering and low-rank regularization. The proposed method solves the ill-posed inverse problem associated with image denoising by the alternating direction method of multipliers. Experimental results demonstrate that the proposed method not only removes noise effectively, but also is highly competitive against the state-of-the-art methods both qualitatively and quantitatively.

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Correspondence to Yongqin Zhang.

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This work was supported by Natural Science Basic Research Program of Shaanxi (Program No. 2019JM-103), Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology (Grant No. LSIT201920W), Social Science Foundation of Shaanxi Province (Grant No. 2019H010), Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT13090), and NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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Zhang, Y., Kang, R., Peng, X. et al. Image denoising via structure-constrained low-rank approximation. Neural Comput & Applic (2020).

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  • Image denoising
  • Sparse representation
  • Low-rank approximation
  • Wiener filtering
  • Deep learning