A General Spatial-Frequency Learning Framework for Multimodal Image Fusion

人工智能 计算机科学 计算机视觉 频域 卷积(计算机科学) 领域(数学分析) 空间分析 锐化 模式识别(心理学) 空间频率 人工神经网络 数学 物理 光学 数学分析 统计
作者
Man Zhou,Jie Huang,Keyu Yan,Danfeng Hong,Xiuping Jia,Jocelyn Chanussot,Chongyi Li
出处
期刊:IEEE Transactions on Pattern Analysis and Machine Intelligence [IEEE Computer Society]
卷期号:: 1-18 被引量:20
标识
DOI:10.1109/tpami.2024.3368112
摘要

multimodal image fusion involves tasks like pan-sharpening and depth super-resolution. Both tasks aim to generate high-resolution target images by fusing the complementary information from the texture-rich guidance and low-resolution target counterparts. They are inborn with reconstructing high-frequency information. Despite their inherent frequency domain connection, most existing methods only operate solely in the spatial domain and rarely explore the solutions in the frequency domain. This study addresses this limitation by proposing solutions in both the spatial and frequency domains. We devise a Spatial-Frequency Information Integration Network, abbreviated as SFINet for this purpose. The SFINet includes a core module tailored for image fusion. This module consists of three key components: a spatial-domain information branch, a frequency-domain information branch, and a dual-domain interaction. The spatial-domain information branch employs the spatial convolution-equipped invertible neural operators to integrate local information from different modalities in the spatial domain. Meanwhile, the frequency-domain information branch adopts a modality-aware deep Fourier transformation to capture the image-wide receptive field for exploring global contextual information. In addition, the dual-domain interaction facilitates information flow and the learning of complementary representations. We further present an improved version of SFINet, SFINet++, that enhances the representation of spatial information by replacing the basic convolution unit in the original spatial domain branch with the information-lossless invertible neural operator. We conduct extensive experiments to validate the effectiveness of the proposed networks and demonstrate their outstanding performance against state-of-the-art methods in two representative multimodal image fusion tasks: pan-sharpening and depth super-resolution. The source code is publicly available at https://github.com/manman1995/Awaresome-pansharpening .
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
清沐完成签到 ,获得积分10
刚刚
科研猫头鹰完成签到,获得积分10
4秒前
dennisysz发布了新的文献求助10
4秒前
11完成签到,获得积分10
5秒前
纯真丁一郎完成签到,获得积分10
5秒前
zyc完成签到 ,获得积分10
6秒前
NN应助yyds采纳,获得10
7秒前
dddd完成签到 ,获得积分10
8秒前
燕子应助科研通管家采纳,获得10
9秒前
iNk应助科研通管家采纳,获得20
9秒前
9秒前
隐形曼青应助科研通管家采纳,获得10
9秒前
Ava应助科研通管家采纳,获得10
9秒前
烟花应助科研通管家采纳,获得10
9秒前
燕子应助科研通管家采纳,获得10
9秒前
科研通AI5应助科研通管家采纳,获得10
10秒前
Akim应助11采纳,获得10
10秒前
共享精神应助王一疯采纳,获得30
11秒前
小点点cy_完成签到 ,获得积分10
12秒前
快乐的小乌龟完成签到,获得积分10
14秒前
15秒前
18秒前
小红完成签到,获得积分10
19秒前
Be-a rogue发布了新的文献求助10
19秒前
19秒前
dywen完成签到,获得积分10
21秒前
wqk完成签到,获得积分10
22秒前
hans发布了新的文献求助10
22秒前
26秒前
粗犷的鹏飞完成签到 ,获得积分10
32秒前
36秒前
i_jueloa完成签到,获得积分10
36秒前
linxe发布了新的文献求助10
39秒前
bkagyin应助CWT采纳,获得10
39秒前
科目三应助犹豫曲奇采纳,获得10
41秒前
41秒前
biofresh完成签到 ,获得积分10
43秒前
852应助景觅波采纳,获得10
44秒前
45秒前
zasideler发布了新的文献求助10
45秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
ISCN 2024 – An International System for Human Cytogenomic Nomenclature (2024) 3000
Continuum Thermodynamics and Material Modelling 2000
Encyclopedia of Geology (2nd Edition) 2000
105th Edition CRC Handbook of Chemistry and Physics 1600
Maneuvering of a Damaged Navy Combatant 650
the MD Anderson Surgical Oncology Manual, Seventh Edition 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3777429
求助须知:如何正确求助?哪些是违规求助? 3322775
关于积分的说明 10211653
捐赠科研通 3038155
什么是DOI,文献DOI怎么找? 1667159
邀请新用户注册赠送积分活动 797971
科研通“疑难数据库(出版商)”最低求助积分说明 758103