Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement

IF 3.4 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing-Image Communication Pub Date : 2024-10-22 DOI:10.1016/j.image.2024.117228
Yueying Luo, Kangjian He, Dan Xu, Hongzhen Shi, Wenxia Yin
{"title":"Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement","authors":"Yueying Luo,&nbsp;Kangjian He,&nbsp;Dan Xu,&nbsp;Hongzhen Shi,&nbsp;Wenxia Yin","doi":"10.1016/j.image.2024.117228","DOIUrl":null,"url":null,"abstract":"<div><div>Effectively fusing infrared and visible images enhances the visibility of infrared target information while capturing visual details. Balancing the brightness and contrast of the fusion image adequately has posed a significant challenge. Moreover, preserving detailed information in fusion images has been problematic. To address these issues, this paper proposes a fusion algorithm based on multi-scale decomposition and adaptive contrast enhancement. Initially, we present a hybrid multi-scale decomposition method aimed at extracting valuable information comprehensively from the source image. Subsequently, we advance an adaptive base layer optimization approach to regulate the brightness and contrast of the resultant fusion image. Lastly, we design a weight mapping rule grounded in saliency detection to integrate small-scale layers, thereby conserving the edge structure within the fusion outcome. Both qualitative and quantitative experimental results affirm the superiority of the proposed method over 11 state-of-the-art image fusion methods. Our method excels in preserving more texture and achieving higher contrast, which proves advantageous for monitoring tasks.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing-Image Communication","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0923596524001292","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Effectively fusing infrared and visible images enhances the visibility of infrared target information while capturing visual details. Balancing the brightness and contrast of the fusion image adequately has posed a significant challenge. Moreover, preserving detailed information in fusion images has been problematic. To address these issues, this paper proposes a fusion algorithm based on multi-scale decomposition and adaptive contrast enhancement. Initially, we present a hybrid multi-scale decomposition method aimed at extracting valuable information comprehensively from the source image. Subsequently, we advance an adaptive base layer optimization approach to regulate the brightness and contrast of the resultant fusion image. Lastly, we design a weight mapping rule grounded in saliency detection to integrate small-scale layers, thereby conserving the edge structure within the fusion outcome. Both qualitative and quantitative experimental results affirm the superiority of the proposed method over 11 state-of-the-art image fusion methods. Our method excels in preserving more texture and achieving higher contrast, which proves advantageous for monitoring tasks.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于混合多尺度分解和自适应对比度增强的红外与可见光图像融合
有效地融合红外图像和可见光图像可以提高红外目标信息的可见度,同时捕捉视觉细节。如何充分平衡融合图像的亮度和对比度是一项重大挑战。此外,在融合图像中保留细节信息也一直是个问题。为了解决这些问题,本文提出了一种基于多尺度分解和自适应对比度增强的融合算法。首先,我们提出了一种混合多尺度分解方法,旨在从源图像中全面提取有价值的信息。随后,我们推进了一种自适应基底层优化方法,以调节融合后图像的亮度和对比度。最后,我们设计了一种基于显著性检测的权重映射规则来整合小尺度层,从而在融合结果中保留边缘结构。定性和定量实验结果都证实了所提出的方法优于 11 种最先进的图像融合方法。我们的方法在保留更多纹理和实现更高对比度方面表现出色,这在监控任务中证明是有利的。
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 去求助
来源期刊
Signal Processing-Image Communication
Signal Processing-Image Communication 工程技术-工程:电子与电气
CiteScore
8.40
自引率
2.90%
发文量
138
审稿时长
5.2 months
期刊介绍: Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
期刊最新文献
Text in the dark: Extremely low-light text image enhancement High efficiency deep image compression via channel-wise scale adaptive latent representation learning Double supervision for scene text detection and recognition based on BMINet A new two-stage low-light enhancement network with progressive attention fusion strategy Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1