A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise

Hanmei Yang, Hanmei Yang, Jiachang Li, Lixin Shen, Jian Lu, Jian Lu

Research output: Contribution to journalArticle

Abstract

This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.

Original languageEnglish (US)
Article number1952782
JournalMathematical Problems in Engineering
Volume2020
DOIs
StatePublished - 2020

ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)

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