IRPol-Fuse: Energy-structure coordination for infrared polarization fusion under low visibility

2026-08-07

AI Summary

This work addresses the limitation of existing infrared-polarization image fusion methods, which overly rely on infrared information under low-visibility conditions, thereby sacrificing polarization texture details. To overcome this, the authors propose IRPol-Fuse, a novel framework that employs an energy-structure collaborative strategy to jointly preserve infrared thermal saliency and polarization structural details. The framework integrates three key components: polarization attention fusion, infrared highlight injection, and polarization texture injection. Additionally, the study introduces LI-PI, the first infrared-polarization benchmark dataset tailored for low-visibility concealed scenarios. Extensive experiments demonstrate that IRPol-Fuse significantly enhances thermal target preservation, structural detail recovery, and visual naturalness on both the LI-PI and LDDRS datasets. The effectiveness of the fused images is further corroborated by improved performance in downstream object detection tasks.

Abstract

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 .

Problem

• infrared-polarization fusion
• low visibility
• structural detail
• thermal saliency
• image fusion

Innovation

• infrared-polarization fusion
• energy-structure coordination
• polarization texture recovery
• low-visibility perception
• attention-based fusion