@Article{NMTMA-15-1,
author = {Min, Liu and Xiliang, Lu},
title = {An Efficient Variational Model for Multiplicative Noise Removal},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2022},
volume = {15},
number = {1},
pages = {125--140},
abstract = {<p style="text-align: justify;">In this paper, an efficient variational model for multiplicative noise removal is proposed. By using a MAP estimator, Aubert and Aujol [SIAM J. Appl.
Math., 68(2008), pp. 925-946] derived a nonconvex cost functional. With logarithmic transformation, we transform the image into a logarithmic domain which makes
the fidelity convex in the transform domain. Considering the TV regularization term
in logarithmic domain may cause oversmoothness numerically, we propose the TV
regularization directly in the original image domain, which preserves more details
of images. An alternative minimization algorithm is applied to solve the optimization problem. The $z$-subproblem can be solved by a closed formula, which makes the
method very efficient. The convergence of the algorithm is discussed. The numerical
experiments show the efficiency of the proposed model and algorithm.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2021-0065},
url = {https://global-sci.com/article/90246/an-efficient-variational-model-for-multiplicative-noise-removal}
}