Multi-Source Image Fusion

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5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

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Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images
May 13, 2026Xingyuan Li, Haoyuan Xu, Xingyue Zhu +4Multi-Source Image Fusion Infrared-Visible Image Fusion
Dalian University of Technology · Northwestern Polytechnical University · Dalian Maritime University
Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization
May 7, 2026Weijian Su, Songqian Zhang, Yuqi Han +3Diffusion Model Alignment Multi-Source Image Fusion
School of Computer Science and Technology, Dalian University of Technology · Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education · Institute of Image Processing and Understanding, North Minzu University
Adding Thermal Awareness to Visual Systems in Real-Time via Distilled Diffusion Models
May 7, 2026Yuchen Guo, Junli Gong, Wenjun Dong +2Multi-Source Image Fusion Infrared-Visible Image Fusion
Northwestern University · Northeastern University · Hong Kong Baptist University +1
FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network
May 4, 2026Jagrati Talreja, Tewodros Syum Gebre, Leila Hashemi-BeniCross-Modal Knowledge Distillation Multi-Source Image Fusion
Geomatics Program, College of Science and Technology, North Carolina A&T Technical State University, Greensboro, North Carolina 27411, USA · Institute for Water, Environment and Health, United Nations University, Richmond Hill, Ontario, Canada
EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion
May 3, 2026Zhenyu Sun, Luobin Zhang, Axi Niu +2Dynamic Convolution Multi-Source Image Fusion
Northwestern Polytechnical University, Xi’an, China
Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
May 1, 2026Ge Luo, Jun-Jie Huang, Qi Yu +6Multi-Source Image Fusion Representation Learning
Semi-Supervised Flow Matching for Mosaiced and Panchromatic Fusion Imaging
Apr 22, 2026Peiming Luo, Nan Wang, Litong Liu +4Flow Matching Multi-Source Image Fusion
Southeast University · Hunan University
EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion
Mar 17, 2026Zhiwei Wang, Defeng He, Li Zhao +3Multi-Source Image Fusion Infrared-Visible Image Fusion
College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China · College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, 310015, China · Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China

May 13, 2026, cs.CVJ/K move · Enter open · S save

Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images

Xingyuan Li, Haoyuan Xu, Xingyue Zhu, Jun Ma+3

Dalian University of Technology · Northwestern Polytechnical University · Dalian Maritime University

Multi-Source Image Fusion Infrared-Visible Image Fusion Frequency-Domain Image Processing Cross-Modal Image Registration Deformable Image Registration

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Infrared and Visible Image Fusion (IVIF) has shown promise in visual tasks under challenging environments, but fusion under unregistered conditions faces inherent misalignments. Current studies to solve them either predict the deformation parameters coarse-to-fine (i.e., coarse registration and fine registration) or estimate the deformation fields in multi-scales for registration. Though straightforward, they overlook the cumulative errors in registration, which contaminate the fusion stage and severely deteriorate the resulting images. We introduce the Spatial-Frequency Registration and Fusion (SFRF) framework, which incorporates uncertainty estimation and infrared thermal radiation distribution consistency into a unified pipeline to handle the error accumulation for robust registration and fusion across both spatial and frequency domains. Specifically, SFRF constructs a Multi-scale Iterative Registration (MIR) framework that iteratively refines the deformation field across scales, leveraging uncertainty estimation at each stage to mitigate error accumulation and enhance alignment accuracy dynamically. To ensure the accurate alignment of infrared thermal distributions during registration, thermal radiation distribution consistency is employed as a frequency-domain supervisory signal, promoting global consistency in the frequency domain. Based on the spatial-frequency alignment, SFRF further adopts a Dual-branch Spatial-Frequency Fusion (DSFF) module, which incorporates spatial geometric features and frequency distribution information to reconstruct visually appealing images. SFRF achieves impressive performance across diverse datasets.

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