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

作     者：Yibin Tang Ying Chen Aimin Jiang Jian Li Yan Zhou Hon Keung Kwan 

作者机构：College of Internet of Things EngineeringHohai UniversityChangzhou213022China School of Microelectronics and Control EngineeringChangzhou UniversityChangzhou213022China Department of Electrical EngineeringUniversity of WindsorOntarioN9B 3P4Canada 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第10期

页      面：67-80页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This work is supported by Natural Science Foundation of Jiangsu Province,China[BK20170306] National Key R&D Program,China[2017YFC0306100].The initials of authors who received these grants are YZ and JL,respectively.It is also supported by Fundamental Research Funds for Central Universities,China[B200202217] Changzhou Science and Technology Program,China[CJ20200065].The initials of author who received these grants are YT 

主　　题：Graph filtering image denoising MAP estimation superpixel 

摘      要：Graph filtering is an important part of graph signal processing and a useful tool for image *** graph filtering methods,such as adaptive weighted graph filtering(AWGF),focus on coefficient shrinkage strategies in a graph-frequency ***,they seldom consider the image attributes in their graph-filtering ***,the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising *** fully exploit the image attributes,we propose a guided intra-patch smoothing AWGF(AWGF-GPS)method for single-image *** AWGF,which employs graph topology on patches,AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a *** 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 ***,a guided-patch regularizer is incorporated into *** guided patch is obtained in advance using a maximum-a-posteriori probability *** 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 *** demonstrate that the AWGF-GPS method suitably rebuilds denoising *** outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning *** particular,it has the advantage of managing images with significant noise.