@article{Jung2026, 
author = {Miyoun Jung},
title = {Color image denoising under mixed multiplicative and Gaussian noise via group-sparse representation and SVTV regularization},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {2},
pages = {3920-3956},
keywords = {color image denoising, multiplicative noise, Gaussian noise, group-based sparse representation, saturation-value total variation, proximal alternating minimization algorithm},
url = {https://www.sciopen.com/article/10.3934/math.2026158},
doi = {10.3934/math.2026158},
abstract = {Color image denoising under the simultaneous presence of multiplicative and Gaussian noise is challenging due to the differing statistical properties of the two noise types. We propose a variational framework that integrates an infimal-convolution-based data-fidelity term with saturation-value total variation (SVTV) and group-based sparse representation (GSR) regularization. By explicitly decoupling the multiplicative and Gaussian noise components, the data-fidelity term enables effective suppression of mixed noise. The two regularizers play complementary roles: SVTV promotes piecewise-smooth reconstructions while preserving edges, whereas GSR enhances fine details and textures and mitigates the staircase artifacts induced by SVTV. The resulting nonconvex optimization problem is addressed using a proximal alternating minimization strategy, with the alternating direction method of multipliers employed to efficiently solve the subproblems. A convergence analysis of the proposed algorithm is provided. Numerical experiments demonstrate that the proposed method consistently outperforms existing approaches for denoising color images corrupted by mixed multiplicative and Gaussian noise.}
}