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Received:October 15, 2021 Revised:November 29, 2021
Received:October 15, 2021 Revised:November 29, 2021
中文摘要: 在图像处理领域, 图像去噪是一项极具挑战性的任务. 图信号理论的发展为我们解决这一问题提供了新的视角. 本文研究了基于图信号方法的权重矩阵与拉普拉斯矩阵, 将它们用于图像去噪的目标函数, 这两个矩阵可以很好地定义观测图像与期望图像之间的内在联系. 在提出去噪目标函数的基础上, 我们给出了最优解和一种迭代的快速求解算法. 实验表明, 该方法优于BM3D和WNNM等前沿的去噪方法.
Abstract:In the field of image processing, image denoising is quite challenging. The development of graph signal theory provides a new perspective for us to solve this problem. In this study, the weight matrix and the Laplace matrix based on a graph signal method are studied, and they are used for the objective function of image denoising. These two matrices can well define the internal relationship between the observed image and the expected image. After proposing the denoising objective function, we give the optimal solution and an iterative fast solution algorithm. Experiments show that this method is superior to cutting-edge denoising methods such as BM3D and WNNM.
keywords: image restoration image denoising graph signal processing Laplace matrix nonlocal mean filtering graph structure
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张哲浩,葛华勇,孙家慧.基于图结构滤波的图像去噪.计算机系统应用,2022,31(7):285-289
ZHANG Zhe-Hao,GE Hua-Yong,SUN Jia-Hui.Image Denoising Using Graph-based Filtering.COMPUTER SYSTEMS APPLICATIONS,2022,31(7):285-289
张哲浩,葛华勇,孙家慧.基于图结构滤波的图像去噪.计算机系统应用,2022,31(7):285-289
ZHANG Zhe-Hao,GE Hua-Yong,SUN Jia-Hui.Image Denoising Using Graph-based Filtering.COMPUTER SYSTEMS APPLICATIONS,2022,31(7):285-289