基于双域信息的深度残差网络图像去噪

doi:10.3969/j.issn.1000-1565.2023.02.014

河北大学学报(自然科学版) ›› 2023, Vol. 43 ›› Issue (2): 216-224.DOI: 10.3969/j.issn.1000-1565.2023.02.014

• • 上一篇

基于双域信息的深度残差网络图像去噪

李凯¹,张辉¹,崔丽娟²,彭锦佳¹,陈泰熙³

收稿日期:2022-01-10 出版日期:2023-03-25 发布日期:2023-04-06
作者简介:李凯(1963—),男,河北保定人,河北大学教授,博士,主要从事机器学习、数据挖掘等研究. E-mail:likai@hbu.cn
基金资助:
河北省自然科学基金资助项目(F2022201009)

Image denoising based on deep residual network with dual-domain information

LI Kai¹, ZHANG Hui¹, CUI Lijuan², PENG Jinjia¹, CHEN Taixi³

1.Hebei Machine Vision Engineering Research Center, School of Cyber Security and Computer, Hebei University, Baoding 071002, China; 2.Library, Hebei University, Baoding 071002, China; 3.Department of Computer Science, Hong Kong Baptist University, Hong Kong 999077, China

Received:2022-01-10 Online:2023-03-25 Published:2023-04-06

摘要/Abstract

摘要： 基于深度学习的去噪技术,通过考虑视觉伪影和整体平滑噪声,提高了图像的质量.然而,它们很少涉及边缘细节的恢复.为此,本文提出了一种基于双域信息的深度残差网络去噪模型,利用小波域信息与空间域信息的融合来扩展网络学习信息,通过在激活单元内引入多尺度学习和空洞卷积,以此提取图像特征,并减少了网络参数.为了进一步改善去噪结果,结合小波域损失和空间域损失构造联合损失函数,使得网络获取更多的边缘与细节.实验结果表明,本文提出的方法不仅可以有效去除图像噪声,而且可以更好地恢复图像纹理细节,在主观和客观评价中均获得了更好的结果.

关键词: 图像去噪, 双域映射, 残差学习, 激活单元, 损失函数

Abstract: The denoising technology based on deep learning improves the image quality by considering visual artifacts and overall smoothing noise. However, they rarely involve the restoration of edge details. In this paper, a deep denoising network model based on dual-domain mapping and residual learning is proposed. The network learning information is extended by the fusion of wavelet domain information and spatial domain information. By introducing multi-scale learning and dilated convolution into the activation unit, the image features are extracted and the network parameters are reduced. In order to further improve the denoising results, the joint loss function of wavelet domain loss and spatial domain loss are combined to guide the network to obtain more edges and details. Experimental results show that the proposed method can not only effectively remove image noise, but also better restore image texture details, and obtain better results in both subjective and objective evaluation.

Key words: image denoising, dual-domain mapping, residual learning, activation unit, loss function

中图分类号:

TP391

李凯,张辉,崔丽娟,彭锦佳,陈泰熙. 基于双域信息的深度残差网络图像去噪[J]. 河北大学学报(自然科学版), 2023, 43(2): 216-224.

LI Kai, ZHANG Hui, CUI Lijuan, PENG Jinjia, CHEN Taixi. Image denoising based on deep residual network with dual-domain information[J]. Journal of Hebei University(Natural Science Edition), 2023, 43(2): 216-224.

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基于双域信息的深度残差网络图像去噪

Image denoising based on deep residual network with dual-domain information

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