A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise. (10th June 2020)

• Record Type: Journal Article
• Authors: Yang, Hanmei; Li, Jiachang; Shen, Lixin; Lu, Jian
• Other Names: Zaldivar Daniel Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-06-10
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/1952782
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.