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Open Access Research Article

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

Liang Xiao*, Li-Li Huang and Zhi-Hui Wei

Author Affiliations

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

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EURASIP Journal on Advances in Signal Processing 2010, 2010:490384  doi:10.1155/2010/490384

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2010/1/490384

Received:29 April 2009
Revisions received:5 October 2009
Accepted:16 February 2010
Published:12 May 2010

© 2010 The Author(s).

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.


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.

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