A novel adaptive non-convex TV $ ^{p,q} $ model in image restoration
Year: 2024
Type: article
Abstract: In this paper, we propose a novel adaptive non-convex TV$ ^{p, q} $ model. The model combines a non-convex regularization with a convex regularization, which can inherit the advantages of the two regularizations, and has a remarkable effect in enhancing image edges and avoiding over-smooth. Simultaneously, the model can eliminate the staircase effect. Moreover, we extend the model to an adaptive form which divides the image into a edge region and a piecewise constant region. In this model, the parameters adaptively change. In terms of algorithm, we employ the iterative support shrinking algorithm to solve these two proposed models. The convergence is also established. Experimental results show that the new non-convex TV$ ^{p, q} $ model and algorithm are effective for image restoration.
Source: Inverse Problems and Imaging
DOI: 10.3934/ipi.2024052
Authors: Bao Chen, Yuchao Tang, Xiaohua Ding
Institutions: Guangzhou University, Harbin Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405467751