发表机构
Foshan University（佛山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对低能见度下红外偏振图像融合的缺陷，提出IRPol-Fuse框架，构建LI-PI数据集，实验验证其在目标与细节保留及下游任务中的优异性能。
AI 中文摘要
低能见度条件下的鲁棒感知需要融合图像同时保留红外热显著性与偏振衍生的结构细节。然而，现有的红外偏振图像融合（IPIF）方法往往过度强调占主导地位的红外响应，导致暗区域中微弱但有价值的偏振纹理被抑制。为解决这一问题，本文提出IRPol-Fuse，一种针对低能见度复杂场景的能量-结构协同IPIF框架。该框架包含三个关键模块：用于自适应红外-偏振分配的偏振注意力融合模块、用于高光引导红外保留的红外高光注入模块、用于偏振纹理恢复与细节修复的偏振纹理注入模块。本文还构建了专用红外偏振评估数据集LI-PI，用于低能见度及视觉隐蔽场景。在LI-PI与公开LDDRS数据集上的实验表明，IRPol-Fuse在热目标保留、结构细节恢复及视觉自然度方面表现优异；区域感知评估与下游目标检测进一步验证，所提出的能量-结构协同策略可有效保留红外目标显著性与偏振衍生结构信息，代码可在指定URL获取。
英文摘要
Robust perception under low-visibility conditions requires fused imagery that jointly preserves infrared thermal saliency and polarization-derived structural details. However, existing infrared-polarization image fusion (IPIF) methods often overemphasize dominant infrared responses, causing weak yet informative polarization textures in dark regions to be suppressed. To address this issue, we propose IRPol-Fuse, an energy-structure coordinated IPIF framework for challenging low-visibility scenarios. The proposed framework contains three key modules: Polarization Attention Fusion for adaptive infrared-polarization allocation, Infrared Highlight Injector for highlight-guided infrared preservation, and Polarization Texture Injector for polarization texture restoration and fine-detail recovery. We further construct LI-PI, a dedicated infrared-polarization evaluation dataset for low-visibility and visually concealed scenes. Experiments on LI-PI and the public LDDRS dataset demonstrate that IRPol-Fuse achieves favorable performance in thermal target preservation, structural detail recovery, and visual naturalness. Region-aware evaluation and downstream object detection further verify that the proposed energy-structure coordination strategy effectively preserves both infrared target saliency and polarization-derived structural information. Code is available at https://github.com/1hzf/IRPolar-Fuse .
DOI：10.1016/j.infrared.2026.106788
