Yibin Tang1, Ying Chen2, Aimin Jiang1, Jian Li1, Yan Zhou1,*, Hon Keung Kwan3
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 67-80, 2021, DOI:10.32604/cmc.2021.017300
Abstract Graph filtering is an important part of graph signal processing and a useful tool for image denoising. Existing graph filtering methods, such as adaptive weighted graph filtering (AWGF), focus on coefficient shrinkage strategies in a graph-frequency domain. However, they seldom consider the image attributes in their graph-filtering procedure. Consequently, the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods. To fully exploit the image attributes, we propose a guided intra-patch smoothing AWGF (AWGF-GPS) method for single-image denoising. Unlike AWGF, which employs graph topology on patches, AWGF-GPS learns the topology of superpixels by introducing the… More >
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