Work

Joint group sparse coding and weighted nuclear norm for Poisson denoising

Year: 2025

Type: article

Abstract: It is difficult to remove Poisson noise because of its multiplicative and signal-dependent nature. In this paper, a new Poisson denoising model based on nonlocal self-similarity is introduced. It combines the weighted nuclear norm and group sparse coding as a regularization term, and makes full use of the low rank and sparse properties of similar image patches. Numerically, incorporating singular value decomposition and the variable splitting method, an alternating minimization method with an adaptive parameter selection strategy is proposed to resolve the new denoising model. Extensive experiments indicate that the proposed model outperforms the existing state-of-the-art Poisson denoising methods.

Source: Journal of Computational and Applied Mathematics

DOI: 10.1016/j.cam.2025.116695

Authors: Jianguang Zhu, Wen Gao, Ying Wei, Binbin Hao

Institutions: Shandong University of Science and Technology, China University of Petroleum, East China

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,250

Funders: Department of Science and Technology of Shandong Province, National Natural Science Foundation of China