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Lookup NU author(s): Professor Jonathon Chambers
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
The multiplicative noise removal problem for a corrupted image has recently been considered under the framework of regularization based approaches, where the regularizations are typically defined on sparse dictionaries and/or total variation (TV). This framework was demonstrated to be effective. However, the sparse regularizers used so far are based overwhelmingly on the synthesis model, and the TV based regularizer may induce the stair-casing effect in the reconstructed image. In this paper, we propose a new method using a sparse analysis model. Our formulation contains a data fidelity term derived from the distribution of the noise and two regularizers. One regularizer employs a learned analysis dictionary, and the other regularizer is an enhanced TV by introducing a parameter to control the smoothness constraint defined on pixel-wise differences. To address the resulting optimization problem, we adapt the alternating direction method of multipliers (ADMM) framework, and present a new method where a relaxation technique is developed to update the variables flexibly with either image patches or the whole image, as required by the learned dictionary and the enhanced TV regularizers, respectively. Experimental results demonstrate the improved performance of the proposed method as compared with several recent baseline methods, especially for relatively high noise levels.
Author(s): Dong J, Han Z-F, Zhao Y, Wang W, Prochazka A, Chambers JA
Publication type: Article
Publication status: Published
Journal: Signal Processing
Year: 2017
Volume: 137
Pages: 160-176
Print publication date: 01/08/2017
Online publication date: 03/02/2017
Acceptance date: 26/01/2017
Date deposited: 09/02/2017
ISSN (print): 0165-1684
ISSN (electronic): 1872-7557
Publisher: Elsevier BV
URL: http://dx.doi.org/10.1016/j.sigpro.2017.01.032
DOI: 10.1016/j.sigpro.2017.01.032
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