A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise

Hanmei Yang, Jiachang Li, Lixin Shen and Jian Lu

Mathematical Problems in Engineering, 2020, vol. 2020, 1-19

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1952782

DOI: 10.1155/2020/1952782

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