@Article{CiCP-13-1066,
author = {Yumei Huang, Michael Ng and Tieyong Zeng},
title = {The Convex Relaxation Method on Deconvolution Model with Multiplicative Noise},
journal = {Communications in Computational Physics},
year = {2013},
volume = {13},
number = {4},
pages = {1066--1092},
abstract = {<p style="text-align: justify;">In this paper, we consider variational approaches to handle the multiplicative noise removal and deblurring problem. Based on rather reasonable physical
blurring-noisy assumptions, we derive a new variational model for this issue. After
the study of the basic properties, we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex
model. The relaxed model is solved by an alternating minimization approach. Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed
method.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.310811.090312a},
url = {http://global-sci.org/intro/article_detail/cicp/7264.html}
}