通过感知区域融合提升极端场景下红外与可见光图像的清晰度
Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement
- 基于区域感知的多模态多曝光图像融合框架
- 显著提升极端环境下的图像清晰度和几何保真度
- 适合遥感、夜视等需要高精度成像的场景
在摄影测量中，如何在极端条件下准确融合红外（IR）与可见光（VIS）图像，同时保持可见光特征的几何精度并融入热辐射信息，仍是一大挑战。现有方法常导致可见光图像质量下降，影响测量准确性。为此，我们提出一种基于区域感知的融合框架，结合多曝光与多模态成像，采用空间可变曝光（SVE）相机采集数据。该框架先通过区域感知实现特征级融合，确保多模态精准配准，再进行自适应融合与对比度增强。基于区域显著性图的结构相似性补偿机制，优化了红外-可见光谱的融合效果。此外，该框架可适配单曝光场景，在不同条件下均表现稳健。在合成与真实数据上的实验表明，该方法在定量与定性评估上均优于当前最先进方法，显著提升了图像清晰度。
原文摘要 · Abstract (English)
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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