Journal of the Korean Society for Industrial and Applied Mathematics

Korean Society for Industrial and Applied Mathematics (한국산업응용수학회)
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SATURATION-VALUE TOTAL VARIATION BASED COLOR IMAGE DENOISING UNDER MIXED MULTIPLICATIVE AND GAUSSIAN NOISE

  • JUNG, MIYOUN (DEPARTMENT OF MATHEMATICS, HANKUK UNIVERSITY OF FOREIGN STUDIES)
  • Received : 2022.07.30
  • Accepted : 2022.09.23
  • Published : 2022.09.25
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Abstract

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

Keywords

Acknowledgement

The author's work was supported in part by the Hankuk University of Foreign Studies Research Fund and in part by the National Research Foundation of Korea (2021R1F1A1048111).

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