图像去噪方法概述

doi:10.3778/j.issn.1673-9418.2101035

摘要/Abstract

摘要：

在现实场景中，由于设备和系统不完善或存在弱光环境导致采集的图像存在噪声，图像在压缩和传输过程中也会受到额外噪声的影响，给后续的图像分割、特征提取等处理造成干扰。传统去噪方法利用图像的非局部自相似性（NLSS）特性和变换域中的稀疏表示，基于块匹配和三维滤波（BM3D）的方法展现出了强大的图像去噪性能。随着人工智能的发展，基于深度学习的图像去噪方法取得了较为突出的表现。但是到目前为止几乎没有相关研究对图像去噪的方法进行全面的比较。针对传统的图像去噪方法及近年来兴起的基于深度神经网络的图像去噪方法，首先介绍了经典的传统去噪和深度神经网络去噪方法的基本框架，并对去噪方法进行了分类总结。然后在公共去噪数据集上对现有的去噪方法进行了定量和定性方面的分析比较。最后在图像去噪领域指出了一些潜在的挑战和未来研究的方向。

关键词: 非局部相似性, 变换域, 块匹配技术, 深度神经网络

Abstract:

In real scenes, due to the imperfections of equipment and systems or the existence of low-light environments, the collected images are noisy. The images will also be affected by additional noise during the compression and transmission process, which will interfere with subsequent image segmentation and feature extraction processes. Traditional denoising methods use the non-local self-similarity (NLSS) characteristics of the image and the sparse representation in the transform domain, and the method based on block-matching and three-dimensional filtering (BM3D) shows a powerful image denoising performance. With the development of artificial intelligence, image denoising methods based on deep learning have achieved outstanding performance. But so far, there is almost no relevant research on the comprehensive comparison of image denoising methods. Aiming at the traditional image denoising methods and the image denoising methods based on deep neural networks that have emerged in recent years, this paper first introduces the basic framework of the classic traditional denoising and deep neural network denoising methods and classifies and summarizes the denoising methods. Then the existing denoising methods are analyzed and compared quantitatively and qualitatively on the public denoising data set. Finally, this paper points out some potential challenges and future research directions in the field of image denoising.

Key words: non-local similarity, transform domain, block matching technology, deep neural network

刘利平, 乔乐乐, 蒋柳成. 图像去噪方法概述[J]. 计算机科学与探索, 2021, 15(8): 1418-1431.

LIU Liping, QIAO Lele, JIANG Liucheng. Overview of Image Denoising Methods[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1418-1431.

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