Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer

Yu Han, Chen Xu, George Baciu, Xiangchu Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

A novel variational model for removing multiplicative noise is proposed in this paper. In the model, a novel regularization term is elaborately designed which is inherently equivalent to a combination of the classical total variation regularizer and a nonconvex regularizer. The proposed regularization term, on the one hand, can better remove the noise in homogeneous regions of a noisy image and, on the other hand, can preserve edge details of the image during the denoising process. In order to solve the model efficiently, we design an alternating iteration process in which two coupling minimization problems are solved. For each of the two minimization problems, the existence and uniqueness of their solutions are proved under some necessary assumptions. Numerical results are reported to demonstrate the effectiveness of the proposed regularization term for multiplicative noise removal.
Original languageEnglish
Pages (from-to)2243-2259
Number of pages17
JournalInternational Journal of Computer Mathematics
Volume91
Issue number10
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • alternating iteration
  • denoising
  • multiplicative noise
  • nonconvex regularizer
  • total variation

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

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