A nonconvex TVcapped-L1 model for image restoration with impulse noise

Yuchen Li; Xueying Zeng

doi:10.1117/12.3057686

13 February 2025 A nonconvex TVcapped-l₁ model for image restoration with impulse noise

Yuchen Li, Xueying Zeng

Author Affiliations +

Proceedings Volume 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024); 1353917 (2025) https://doi.org/10.1117/12.3057686
Event: Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 2024, Nanjing, China

Abstract

This paper addresses the problem of image deblurring in the presence of impulse noise. While methods based on total variation regularization have a long-standing history in image processing, the traditional convex TVL1 model that using the L₁ fidelity may introduce bias in the recovery results. To overcome this drawback, we propose a novel image deblurring model for impulse noise, called TVcapped-L₁ model, which integrates the nonconvex capped-L₁ fidelity with total variation regularization. We also provide an algorithmic framework that employs the difference-of-convex algorithm to solve the proposed model. Numerical experiments demonstrate that our proposed method outperforms existing methods in recovering images degraded by Gaussian blur and various types of impulse noise.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuchen Li and Xueying Zeng "A nonconvex TVcapped-l₁ model for image restoration with impulse noise", Proc. SPIE 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 1353917 (13 February 2025); https://doi.org/10.1117/12.3057686

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