Cauchy Noise Removal by Nonconvex ADMM with Convergence Guarantees

Jin Jin Mei, Yiqiu Dong*, Ting Zhu Huang, Wotao Yin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Image restoration is one of the essential tasks in image processing. In order to restore images from blurs and noise while also preserving their edges, one often applies total variation (TV) minimization. Cauchy noise, which frequently appears in engineering applications, is a kind of impulsive and non-Gaussian noise. Removing Cauchy noise can be achieved by solving a nonconvex TV minimization problem, which is difficult due to its nonconvexity and nonsmoothness. In this paper, we adapt recent results in the literature and develop a specific alternating direction method of multiplier to solve this problem. Theoretically, we establish the convergence of our method to a stationary point. Experimental results demonstrate that the proposed method is competitive with other methods in visual and quantitative measures. In particular, our method achieves higher PSNRs for 0.5 dB on average.

Original languageEnglish
JournalJournal of Scientific Computing
Volume74
Issue number2
Pages (from-to)743-66
ISSN0885-7474
DOIs
Publication statusPublished - 30 May 2017

Keywords

  • Alternating direction method of multiplier
  • Image restoration
  • Kurdyka–Łojasiewicz
  • Nonconvex variational model
  • Total variation

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