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Sparse Analysis Model Based Multiplicative Noise Removal with Enhanced Regularization

Dong, J, Han, Z, Zhao, Y, Wang, Wenwu, Prochazka, A and Chambers, J (2017) Sparse Analysis Model Based Multiplicative Noise Removal with Enhanced Regularization Signal Processing, 137. pp. 160-176.

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Abstract

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 de ned on sparse dictionaries and/or total va- riation (TV). This framework was demonstrated to be e ective. However, the sparse regularizers used so far are based overwhelmingly on the synthesis model, and the TV based regularizer may induce the stair-casing e ect in the recon- structed image. In this paper, we propose a new method using a sparse analysis model. Our formulation contains a data delity term derived from the distri- bution 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 de ned on pixel-wise di er- ences. 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 exibly with either image patches or the whole image, as required by the learned dictionary and the enhanced TV regularizers, respectively. Experimental results demon- strate the improved performance of the proposed method as compared with several recent baseline methods, especially for relatively high noise levels.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Dong, JUNSPECIFIEDUNSPECIFIED
Han, ZUNSPECIFIEDUNSPECIFIED
Zhao, YUNSPECIFIEDUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Prochazka, AUNSPECIFIEDUNSPECIFIED
Chambers, JUNSPECIFIEDUNSPECIFIED
Date : 3 February 2017
Identification Number : 10.1016/j.sigpro.2017.01.032
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Multiplicative noise, analysis sparse model, dictionary learning, smoothness regularizer
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 15 Feb 2017 09:16
Last Modified : 07 Jul 2017 08:29
URI: http://epubs.surrey.ac.uk/id/eprint/813545

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