@Article{IJNAM-22-21,
author = {Zhao , WufanWu , TingtingFeng , ChenchenWu , WennaLv , XiaoguangChen , Hongming and Liu , Jun},
title = {Image Smoothing via a Novel Adaptive Weighted $L_0$ Regularization},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2024},
volume = {22},
number = {1},
pages = {21--39},
abstract = {<p style="text-align: justify;">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 $L_0$ gradient intensity.
Specifically, a weighted matrix $T$ is imposed to control further the sparsity of the gradient measured by $L_0$-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.</p>},
issn = {2617-8710},
doi = {https://doi.org/10.4208/ijnam2025-1002},
url = {http://global-sci.org/intro/article_detail/ijnam/23565.html}
}