On the Convex Model of Speckle Reduction

Faming Fang, Yingying Fang, Tieyong ZENG*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Speckle reduction is an important issue in image processing realm. In this paper, we propose a novel model for restoring degraded images with multiplicative noise which follows a Nakagami distribution. A general penalty term based on the statistical property of the speckle noise is used to guarantee the convexity of the denoising model. Moreover, to deal with the minimizing problem, a generalized Bermudez-Moreno algorithm is adopted and its convergence is analysed. The experimental results on some images subject to multiplicative noise as well as comparisons to other state-of-the-art methods are also presented. The results can verify that the new model is reasonable.

Original languageEnglish
Pages (from-to)121-141
Number of pages21
JournalMathematics and Visualization
Volume0
Issue number221219
DOIs
Publication statusPublished - 2018
EventInternational conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016 - Bergen, Norway
Duration: 29 Aug 20162 Sep 2016

Scopus Subject Areas

  • Modelling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'On the Convex Model of Speckle Reduction'. Together they form a unique fingerprint.

Cite this