Nonlocal low-rank regularized two-phase approach for mixed noise removal

Chen Xu, Xiaoxia Liu, Jian Zheng, Lixin Shen, Qingtang Jiang, Jian Lu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Removing mixed noise in images is a difficult problem which has been discussed in many recent papers. In this paper, we tackle the problem of having mixed additive Gaussian white noise and impulse noise. We propose to remove this mixed noise through a nonlocal low-rank regularized two-phase approach. In the first phase, we identify and label the pixels that are likely to be corrupted by the impulse noise. In the second phase, the image is restored through the unlabeled observed data. The restored image is achieved via solving an optimization problem whose objective function has an ℓ 1/ℓ 2 combined content-dependent fidelity term and a nonconvex nonlocal low-rank regularization term. Both terms are built on patch matrices formed from similar patches. Each patch matrix is considered to be low-rank according to the prior knowledge of images. We solve this nonconvex optimization through an iterative adaptive nuclear norm minimization algorithm and provide its convergence analysis. Our experiments show the proposed method outperforms the existing state-of-the-art algorithms in terms of three quantitative metrics, namely, the peak signal-to-noise ratio, the structural similarity and the feature similarity, and visual quality of the restored images.

Original languageEnglish (US)
Article number085001
JournalInverse Problems
Volume37
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • Low-rank
  • Mixed noise removal
  • Nonlocal self-similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Mathematical Physics
  • Computer Science Applications
  • Applied Mathematics

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