Toggle Main Menu Toggle Search

Open Access padlockePrints

Sparse analysis model based multiplicative noise removal with enhanced regularization

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.

Publication metadata

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


DOI: 10.1016/j.sigpro.2017.01.032


Altmetrics provided by Altmetric