Ying Chen1, 2, Yibin Tang3, Lin Zhou1, Yan Zhou3, 4, Jinxiu Zhu3, Li Zhao1, *
CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1219-1232, 2020, DOI:10.32604/cmc.2020.010638
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… More >
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