光学精密工程2026，Vol.34Issue(7)：1142-1155,14.DOI:10.37188/OPE.20263407.1142
可靠性自适应引导的红外与可见光图像融合
Reliability adaptive guided infrared and visible image fusion
摘要
Abstract
To mitigate perception degradation in autonomous driving caused by illumination variations and cross-modal interference,an infrared-visible image fusion network with reliability-adaptive guidance is pro-posed.A pixel-level reliability estimation mechanism is constructed by jointly modeling structural consis-tency and intensity anomalies,enabling dynamic assessment of source credibility.A"trusted injection"strategy is introduced to correct the global intensity distribution,while adaptive guided filtering enhances the competition between salient objects and texture details in the detail layer;the process is optimizedתועצמא ב a multi-constrained loss function.Experiments on the M3FD and RoadScene datasets demon-strate that,compared with DWT,GTF,U2Fusion,and Umcfuse,the proposed method improves EN,SD,SF,AG,MI,Qabf,EI,and VIFF by 1.51％,16.56％,42.36％,52.24％,38.28％,80.51％,21.4％,and 17.6％,respectively.In downstream target detection tasks,an average accuracy of 91.4％is achieved,surpassing existing fusion methods.The proposed approach effectively suppresses artifacts and noise,exhibits strong scene generalization and robustness,and significantly enhances environmental perception accuracy in autonomous driving systems.关键词
图像融合/红外与可见光/可靠性自适应引导/跨模态结构一致性/可信注入/自动驾驶感知Key words
image fusion/infrared and visible/reliability-adaptive guidance/cross-modal structural con-sistency/trusted injection/autonomous driving perception分类
信息技术与安全科学引用本文复制引用
王琛,马庆禄,周志超,刘明..可靠性自适应引导的红外与可见光图像融合[J].光学精密工程,2026,34(7):1142-1155,14.基金项目
国家自然科学基金资助项目(No.52072054) （No.52072054）
重庆市交通科技资助项目(No.CQJT-CZKJ2025-07) （No.CQJT-CZKJ2025-07）
重庆市2025研究生科研创新项目(No.CYS25535) （No.CYS25535）
重庆市自然科学基金面上项目(No.CSTB2023NSCQ-MSX0551) （No.CSTB2023NSCQ-MSX0551）
