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

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  • Hanmei Yang
  • Jiachang Li
  • Lixin Shen
  • Jian Lu

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.

Suggested Citation

  • Hanmei Yang & Jiachang Li & Lixin Shen & Jian Lu, 2020. "A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-19, June.
    • Handle: RePEc:hin:jnlmpe:1952782
    DOI: 10.1155/2020/1952782
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