Skip to content


  • Research Article
  • Open Access

A Weberized Total Variation Regularization-Based Image Multiplicative Noise Removal Algorithm

EURASIP Journal on Advances in Signal Processing20102010:490384

  • Received: 29 April 2009
  • Accepted: 16 February 2010
  • Published:


Multiplicative noise removal is of momentous significance in coherent imaging systems and various image processing applications. This paper proposes a new nonconvex variational model for multiplicative noise removal under the Weberized total variation (TV) regularization framework. Then, we propose and investigate another surrogate strictly convex objective function for Weberized TV regularization-based multiplicative noise removal model. Finally, we propose and design a novel way of fast alternating optimizing algorithm which contains three subminimizing parts and each of them permits a closed-form solution. Our experimental results show that our algorithm is effective and efficient to filter out multiplicative noise while well preserving the feature details.


  • Objective Function
  • Total Variation
  • Imaging System
  • Variational Model
  • Quantum Information

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

603 LAB, School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China


© Liang Xiao et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.