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arXiv · 2510.13067

Direction-aware multi-scale gradient loss for infrared and visible image fusion

Kaixuan Yang·Wei Xiang·Zhenshuai Chen·Tong Jin·Yunpeng Liu
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Abstract

Infrared and visible image fusion aims to integrate complementary information from co-registered source images to produce a single, informative result. Most learning-based approaches train with a combination of structural similarity loss, intensity reconstruction loss, and a gradient-magnitude term. However, collapsing gradients to their magnitude removes directional information, yielding ambiguous supervision and suboptimal edge fidelity. We introduce a direction-aware, multi-scale gradient loss that supervises horizontal and vertical components separately and preserves their sign across scales. This axis-wise, sign-preserving objective provides clear directional guidance at both fine and coarse resolutions, promoting sharper, better-aligned edges and richer texture preservation without changing model architectures or training protocols. Experiments on open-source model and multiple public benchmarks demonstrate effectiveness of our approach.

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Kaixuan Yang, Wei Xiang, Zhenshuai Chen, Tong Jin, Yunpeng Liu. 2025-10-15. Direction-aware multi-scale gradient loss for infrared and visible image fusion. https://arxiv.org/abs/2510.13067

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