Infrared and visible image fusion aims to synthesize a fused image that emphasizes the salient objects while retaining the intricate texture and visual quality from both infrared and visible images. In opposite to the majority of existing deep learning-based fusion approaches, which predominantly focus on spatial information and neglect the valuable frequency information, we propose a novel method that delves into both domains simultaneously to tackle the infrared and visible image fusion task. Specifically, we first analyze the frequency characteristics of the two modality images via Fourier transform, and observe that fusion results with complementary attributes from source images can be effectively attained by directly incorporating their phase components. To this end, we propose a Frequency Integration and Spatial Compensation Network (FISCNet), consisting of two core designs: a frequency integration component and a spatial compensation component. The former integrates prominent objects from the source images while maintaining the visual perception from the visible image in the frequency domain, and the latter improves the detailed texture and emphasizes the salient objects through a meticulous compensation mechanism in the spatial domain. Extensive experiments on various benchmarks demonstrate the superiority of our method over state-of-the-art alternatives in terms of both salience preservation and texture fidelity. Code is available at .

中文翻译：

---

用于红外和可见光图像融合的频率积分和空间补偿网络

红外和可见光图像融合旨在合成融合图像，强调显着物体，同时保留红外和可见光图像的复杂纹理和视觉质量。与大多数现有的基于深度学习的融合方法主要关注空间信息而忽略有价值的频率信息相反，我们提出了一种同时深入两个领域来解决红外和可见光图像融合任务的新方法。具体来说，我们首先通过傅里叶变换分析两个模态图像的频率特性，并观察到通过直接合并它们的相位分量可以有效地获得与源图像互补属性的融合结果。为此，我们提出了频率积分和空间补偿网络（FISCNet），由两个核心设计组成：频率积分组件和空间补偿组件。前者整合了源图像中的突出物体，同时在频域中保持了可见图像的视觉感知，后者通过空间域中细致的补偿机制改善了细节纹理并强调了突出物体。对各种基准的广泛实验证明了我们的方法在显着性保留和纹理保真度方面优于最先进的替代方法。代码可在 处获取。