Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8900
Title: Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer
Authors: Han, Y
Xu, C
Baciu, G 
Feng, X
Keywords: Alternating iteration
Denoising
Multiplicative noise
Nonconvex regularizer
Total variation
Issue Date: 2014
Journal: International Journal of Computer Mathematics 
Abstract: A novel variational model for removing multiplicative noise is proposed in this paper. In the model, a novel regularization term is elaborately designed which is inherently equivalent to a combination of the classical total variation regularizer and a nonconvex regularizer. The proposed regularization term, on the one hand, can better remove the noise in homogeneous regions of a noisy image and, on the other hand, can preserve edge details of the image during the denoising process. In order to solve the model efficiently, we design an alternating iteration process in which two coupling minimization problems are solved. For each of the two minimization problems, the existence and uniqueness of their solutions are proved under some necessary assumptions. Numerical results are reported to demonstrate the effectiveness of the proposed regularization term for multiplicative noise removal.
URI: http://hdl.handle.net/10397/8900
ISSN: 0020-7160
DOI: 10.1080/00207160.2013.871002
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