Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement

• URL: http://arxiv.org/abs/2512.06400v1
• Date: Sat, 06 Dec 2025 11:17:35 GMT
• Title: Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement
• Authors: Jing Tao, Yonghong Zong, Banglei Guana, Pengju Sun, Taihang Lei, Yang Shanga, Qifeng Yu,
• Abstract summary: We propose a region perception-based fusion framework that combines multi-exposure and multi-modal imaging.<n>This framework co-fuses multi-modal and multi-exposure data, overcoming single-exposure method limitations in extreme environments.<n> Experiments conducted on both synthetic and real-world data demonstrate superior image clarity and improved performance compared to state-of-the-art methods.
• Score: 8.10747908396949
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In photogrammetry, accurately fusing infrared (IR) and visible (VIS) spectra while preserving the geometric fidelity of visible features and incorporating thermal radiation is a significant challenge, particularly under extreme conditions. Existing methods often compromise visible imagery quality, impacting measurement accuracy. To solve this, we propose a region perception-based fusion framework that combines multi-exposure and multi-modal imaging using a spatially varying exposure (SVE) camera. This framework co-fuses multi-modal and multi-exposure data, overcoming single-exposure method limitations in extreme environments. The framework begins with region perception-based feature fusion to ensure precise multi-modal registration, followed by adaptive fusion with contrast enhancement. A structural similarity compensation mechanism, guided by regional saliency maps, optimizes IR-VIS spectral integration. Moreover, the framework adapts to single-exposure scenarios for robust fusion across different conditions. Experiments conducted on both synthetic and real-world data demonstrate superior image clarity and improved performance compared to state-of-the-art methods, as evidenced by both quantitative and visual evaluations.

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