基于融合曲线的零样本红外与可见光图像融合方法  

Zero-Shot Infrared and Visible Image Fusion Based on Fusion Curve

作　　者：刘铎 张国印[1] 史一岐 田野[2] 张立国[1] LIU Duo;ZHANG Guoyin;SHI Yiqi;TIAN Ye;ZHANG Liguo(College of Computer Science and Technology,Harbin Engineering University,Harbin 150001;Hangzhou Institute of Technology,Xidian University,Hangzhou 311231)

机构地区：[1]哈尔滨工程大学计算机科学与技术学院,哈尔滨150001 [2]西安电子科技大学杭州研究院,杭州311231

出　　处：《模式识别与人工智能》2025年第3期268-279,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家重点研发计划项目(No.2021YFC3320302)资助。

摘　　要：针对红外与可见光图像融合中的颜色失真和热目标细节丢失问题,提出基于融合曲线的零样本红外与可见光图像融合方法(Zero-Shot Infrared and Visible Image Fusion Based on Fusion Curve,ZSFuCu).首先,将融合任务转化为基于深度网络的图像特定曲线估计过程,通过像素级非线性映射实现热目标纹理的增强与色彩特征的保留.然后,设计多维度视觉感知损失函数,从对比度增强、颜色保持及空间连续性三个维度构建约束机制,协同优化融合图像的高频信息与色彩分布,保留结构特征和关键信息.最后,采用零样本训练策略,仅需单个红外与可见光图像对即可完成参数的自适应优化,具备在不同照明条件下融合的强鲁棒性.实验表明,ZSFuCu在目标突出性、细节丰富度及颜色自然度方面具有显著优势,兼具有效性与实用性.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.

关 键 词：红外与可见光图像融合(IVIF) 深度学习 多维度视觉感知 零样本学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]