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Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

Yibin Tang1, Ying Chen2, Aimin Jiang1, Jian Li1, Yan Zhou1,*, Hon Keung Kwan3

1 College of Internet of Things Engineering, Hohai University, Changzhou, 213022, China
2 School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213022, China
3 Department of Electrical Engineering, University of Windsor, Ontario, N9B 3P4, Canada

* Corresponding Author: Yan Zhou. Email:

Computers, Materials & Continua 2021, 69(1), 67-80.


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 pixel smoothing attribute of a patch. This operation forces the restored pixels to smoothly evolve in local areas, where both intra- and inter-patch relationships of the image are utilized during patch restoration. Meanwhile, a guided-patch regularizer is incorporated into AWGF-GPS. The guided patch is obtained in advance using a maximum-a-posteriori probability estimator. Because the guided patch is considered as a sketch of a denoised patch, AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch. Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images. It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods. In particular, it has the advantage of managing images with significant noise.


Cite This Article

Y. Tang, Y. Chen, A. Jiang, J. Li, Y. Zhou et al., "Guided intra-patch smoothing graph filtering for single-image denoising," Computers, Materials & Continua, vol. 69, no.1, pp. 67–80, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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