EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion

Abstract

Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in visual perception. Infrared and visible image fusion can compensate for such degradation via multimodal complementarity. However, most fusion methods lack region-aware optimization for overexposed areas and cannot effectively exploit infrared cues in saturated regions, resulting in insufficient infrared detail preservation or redundant information in the fused results. To address this, we propose EPOFusion, an exposure-aware fusion framework. It employs a spatial guidance module to identify regions requiring infrared compensation, together with a region-aware fusion loss to strengthen informative infrared structures. In addition, an iterative feature refinement head equipped with a multiscale context fusion module progressively refines fused representations, enabling effective integration of complementary infrared information while maintaining visual consistency in normally exposed regions. The infrared and visible overexposure (IVOE) dataset consists of a synthetic training subset providing infrared-compensation supervision and a real-world subset for fusion and downstream perception evaluation under authentic overexposure. EPOFusion demonstrates superior VIF and $Q^{AB/F}$ performance with favorable visual quality, improving $Q^{AB/F}$ by 10.7% over the existing overexposure-oriented fusion baseline, while further improving downstream mIoU and mAP50 by 5.6% and 6.5%, respectively. Code, results, and the IVOE dataset will be made available at https://warren-wzw.github.io/EPOFusion/.