Zero-Shot Infrared and Visible Image Fusion Based on Fusion Curve
LIU Duo1, ZHANG Guoyin1, SHI Yiqi1, TIAN Ye2, ZHANG Liguo1
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001; 2. Hangzhou Institute of Technology, Xidian University, Hangzhou 311231
Abstract To solve the problems of color distortion and the loss of thermal target details in infrared and visible image fusion, a method for zero-shot infrared and visible image fusion based on fusion curve(ZSFuCu) is proposed. The fusion task is transformed into an image-specific curve estimation process using a deep network. Texture enhancement and color feature preservation of thermal targets are achieved through pixel-level nonlinear mapping. A multi-dimensional visual perception loss function is designed to construct the constrain mechanism from three perspectives: contrast enhancement, color preservation and spatial continuity. The high-frequency information and color distribution of the fused image are collaboratively optimized with the retention of structural features and key information. The zero-shot training strategy is employed, and the adaptive optimization of parameters can be completed only using a single infrared and visible image pair, which shows strong robustness in fusion across various lighting conditions. Experiments demonstrate that ZSFuCu significantly improves target prominence, detail richness and color naturalness, validating its effectiveness and practicality.
Fund:National Key Research and Development Program of China(No.2021YFC3320302)
Corresponding Authors:ZHANG Liguo, Ph.D., professor. His research interests include deep learning, machine learning and computer vision.
About author:: LIU Duo, Ph.D. candidate. His research interests include image processing and image fusion. ZHANG Guoyin, Ph.D., professor. His research interests include deep learning and machine learning. SHI Yiqi, Ph.D. candidate. Her research interests include image processing and self-supervised learning. TIAN Ye, Ph.D. His research interests include image processing and intelligent adversarial techniques.
LIU Duo,ZHANG Guoyin,SHI Yiqi等. Zero-Shot Infrared and Visible Image Fusion Based on Fusion Curve[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(3): 268-279.
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