@Article{cmc.2020.010638,
AUTHOR = {Ying Chen, Yibin Tang, Lin Zhou, Yan Zhou, Jinxiu Zhu, Li Zhao},
TITLE = {Image Denoising with Adaptive Weighted Graph Filtering},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1219--1232},
URL = {http://www.techscience.com/cmc/v64n2/39356},
ISSN = {1546-2226},
ABSTRACT = {Graph filtering, which is founded on the theory of graph signal processing, is 
proved as a useful tool for image denoising. Most graph filtering methods focus on learning
an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by 
retaining the image components in low graph frequency bands. However, this lowpass filter 
has limited ability to separate the low-frequency noise from clean images such that it makes 
the denoising procedure less effective. To address this issue, we propose an adaptive 
weighted graph filtering (AWGF) method to replace the design of traditional ideal lowpass 
filter. In detail, we reassess the existing low-rank denoising method with adaptive 
regularizer learning (ARLLR) from the view of graph filtering. A shrinkage approach 
subsequently is presented on the graph frequency domain, where the components of noisy
image are adaptively decreased in each band by calculating their component significances. 
As a result, it makes the proposed graph filtering more explainable and suitable for 
denoising. Meanwhile, we demonstrate a graph filter under the constraint of subspace 
representation is employed in the ARLLR method. Therefore, ARLLR can be treated as a 
special form of graph filtering. It not only enriches the theory of graph filtering, but also 
builds a bridge from the low-rank methods to the graph filtering methods. In the 
experiments, we perform the AWGF method with a graph filter generated by the classical 
graph Laplacian matrix. The results show our method can achieve a comparable denoising 
performance with several state-of-the-art denoising methods.},
DOI = {10.32604/cmc.2020.010638}
}