Image Smoothing via a Novel Adaptive Weighted L0 Regularization
Year: 2025
Author: Wufan Zhao, Tingting Wu, Chenchen Feng, Wenna Wu, Xiaoguang Lv, Hongming Chen, Jun Liu
International Journal of Numerical Analysis and Modeling, Vol. 22 (2025), Iss. 1 : pp. 21–39
Abstract
Image smoothing has been extensively used in various fields, e.g., edge extraction, image abstraction, and image detail enhancement. Many existing optimization-based image smoothing methods have been proposed in recent years. The downside of these methods is that the results often have unclear edges and missing structures. To obtain satisfactory smoothing results, we design a novel optimization model by introducing an anisotropic L0 gradient intensity. Specifically, a weighted matrix T is imposed to control further the sparsity of the gradient measured by L0-norm. Since the proposed model is non-convex and non-smooth, we apply the half quadratic splitting (HQS) algorithm to solve it effectively. In addition, to obtain a more suitable regularization parameter λ, we utilize an adaptive parameter selection method based on Morozovs discrepancy principle. Finally, we conduct numerical experiments to illustrate the superiority of our method over some state-of-the-art methods.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/ijnam2025-1002
International Journal of Numerical Analysis and Modeling, Vol. 22 (2025), Iss. 1 : pp. 21–39
Published online: 2025-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 19
Keywords: Image smoothing adaptive weighted matrix L0 gradient minimization parameter selection.
Author Details
Wufan Zhao Email
Tingting Wu Email
Chenchen Feng Email
Wenna Wu Email
Xiaoguang Lv Email
Hongming Chen Email
Jun Liu Email