Efficient degradation representation learning network for remote sensing image super-resolution

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xuan Wang , Lijun Sun , Jinglei Yi , Yongchao Song , Qiang Zheng , Abdellah Chehri
{"title":"Efficient degradation representation learning network for remote sensing image super-resolution","authors":"Xuan Wang ,&nbsp;Lijun Sun ,&nbsp;Jinglei Yi ,&nbsp;Yongchao Song ,&nbsp;Qiang Zheng ,&nbsp;Abdellah Chehri","doi":"10.1016/j.cviu.2024.104182","DOIUrl":null,"url":null,"abstract":"<div><div>The advancements in convolutional neural networks have led to significant progress in image super-resolution (SR) techniques. Nevertheless, it is crucial to acknowledge that current SR methods operate under the assumption of bicubic downsampling as a degradation factor in low-resolution (LR) images and train models accordingly. However, this approach does not account for the unknown degradation patterns present in real-world scenes. To address this problem, we propose an efficient degradation representation learning network (EDRLN). Specifically, we adopt a contrast learning approach, which enables the model to distinguish and learn various degradation representations in realistic images to obtain critical degradation information. We also introduce streamlined and efficient pixel attention to strengthen the feature extraction capability of the model. In addition, we optimize our model with mutual affine convolution layers instead of ordinary convolution layers to make it more lightweight while minimizing performance loss. Experimental results on remote sensing and benchmark datasets show that our proposed EDRLN exhibits good performance for different degradation scenarios, while the lightweight version minimizes the performance loss as much as possible. The Code will be available at: <span><span>https://github.com/Leilei11111/EDRLN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104182"},"PeriodicalIF":4.3000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002637","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The advancements in convolutional neural networks have led to significant progress in image super-resolution (SR) techniques. Nevertheless, it is crucial to acknowledge that current SR methods operate under the assumption of bicubic downsampling as a degradation factor in low-resolution (LR) images and train models accordingly. However, this approach does not account for the unknown degradation patterns present in real-world scenes. To address this problem, we propose an efficient degradation representation learning network (EDRLN). Specifically, we adopt a contrast learning approach, which enables the model to distinguish and learn various degradation representations in realistic images to obtain critical degradation information. We also introduce streamlined and efficient pixel attention to strengthen the feature extraction capability of the model. In addition, we optimize our model with mutual affine convolution layers instead of ordinary convolution layers to make it more lightweight while minimizing performance loss. Experimental results on remote sensing and benchmark datasets show that our proposed EDRLN exhibits good performance for different degradation scenarios, while the lightweight version minimizes the performance loss as much as possible. The Code will be available at: https://github.com/Leilei11111/EDRLN.
用于遥感图像超分辨率的高效降级表示学习网络
卷积神经网络的进步使图像超分辨率(SR)技术取得了重大进展。然而,必须承认的是,当前的超分辨率方法是在假设低分辨率(LR)图像的降解因素为双三次降采样的情况下运行的,并据此训练模型。然而,这种方法并没有考虑到真实世界场景中存在的未知降解模式。为了解决这个问题,我们提出了一种高效降解表示学习网络(EDRLN)。具体来说,我们采用了一种对比学习方法,使模型能够区分和学习现实图像中的各种退化表征,从而获得关键的退化信息。我们还引入了精简高效的像素关注,以加强模型的特征提取能力。此外,我们用互仿卷积层代替普通卷积层对模型进行了优化,使其更加轻便,同时将性能损失降到最低。在遥感和基准数据集上的实验结果表明,我们提出的 EDRLN 在不同的退化场景下都表现出了良好的性能,而轻量级版本则尽可能减少了性能损失。代码见:https://github.com/Leilei11111/EDRLN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信