Remove the salt and pepper noise based on the high order total variation and the nuclear norm regularization

Baoli Shi, Fang Gu, Zhi-Feng Pang and Yuhua Zeng

Applied Mathematics and Computation, 2022, vol. 421, issue C

Abstract: This paper proposes a new model to remove the salt and pepper (SAP) noise problem. In the proposed method, we combine the high order total variation regularization with the nuclear norm regularization in order to keep details and structures of the restored images. Since the proposed model is convex and separable, the classic alternating direction method of multipliers can be employed to solve it by introducing some auxiliary variables to transform the original problem into the saddle point problem. Theoretically, we establish the convergence analysis of the proposed numerical algorithm. Final experimental comparisons are provided to show the satisfactory performance of the proposed model, which outperforms other related competitive methods in both the SNR and the SSIM.

Keywords: Image denoising; Salt and pepper (SAP) noise; High order total variation (HOTV) regularization; Nuclear norm regularization; Alternating direction method of multipliers (ADMM) (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:421:y:2022:i:c:s009630032200011x

DOI: 10.1016/j.amc.2022.126925

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