@article{dab7061886584c2eb3a9224a32ba2b93,
title = "A New Total Variation Method for Multiplicative Noise Removal",
abstract = "Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, the noise is multiplied to the orginal image, so almost all information of the original image may disappear in the observed image. The main aim of this paper is to propose and study a strictly convex objective function for multiplicative noise removal problems. We also incorporate the modified total variation regularization in the objective function to recover image edges. We develop an alternating minimization algorithm to find the minimizer of such an objective function efficiently and also show the convergence of the minimizing method. Our experimental results show that the quality of images denoised by the proposed method is quite good.",
keywords = "Convex function, Image denoising, Multiplicative noise, Total variation",
author = "Huang, {Yu Mei} and Ng, {Michael K.} and Wen, {You Wei}",
note = "Funding Information: Corresponding author. Institute for Computational Mathematics and Centre for Mathematical Imaging and Vision, Hong Kong Baptist University, Kowloon Tong, Hong Kong (
[email protected]). This author{\textquoteright}s research was supported in part by RGC 7045/05P and 201508 and by HKBU FRGs. Faculty of Science, South China Agricultural University, Wushan, Guangzhou, People{\textquoteright}s Republic of China. Current address: Temasek Laboratories and Department of Mathematics, National University of Singapore, Singapore 117543, Singapore (
[email protected]). This author{\textquoteright}s research was supported in part by NSFC grant 60702030 and the wavelets and information processing program under a grant from DSTA, Singapore. Publisher copyright: Copyright {\textcopyright} 2009 Society for Industrial and Applied Mathematics",
year = "2009",
month = jan,
day = "14",
doi = "10.1137/080712593",
language = "English",
volume = "2",
pages = "20--40",
journal = "SIAM Journal on Imaging Sciences",
issn = "1936-4954",
publisher = "Society for Industrial and Applied Mathematics (SIAM)",
number = "1",
}