Visible and near-infrared(NIR) band sensors provide images that capture complementary spectral radiations from a scene. And the fusion of the visible and NIR image aims at utilizing their spectrum properties to enhance image quality. However, currently visible and NIR fusion algorithms cannot well take advantage of spectrum properties, as well as lack information complementarity, which results in color distortion and artifacts. Therefore, this paper designs a complementary fusion model from the level of physical signals. First, in order to distinguish between noise and useful information, we use two layers of the weight-guided filter and guided filter to obtain texture and edge layers, respectively. Second, to generate the initial visible-NIR complementarity weight map, the difference maps of visible and NIR are filtered by the extend-DoG filter. After that, the significant region of NIR night-time compensation guides the initial complementarity weight map by the arctanI function. Finally, the fusion images can be generated by the complementarity weight maps of visible and NIR images, respectively. The experimental results demonstrate that the proposed algorithm can not only well take advantage of the spectrum properties and the information complementarity, but also avoid color unnatural while maintaining naturalness, which outperforms the state-of-the-art.

翻译：可见光和近红外波段传感器可捕获场景中互补的光谱辐射信息，而可见光与近红外图像融合旨在利用其光谱特性提升图像质量。然而，现有可见光与近红外融合算法未能充分利用光谱特性，且缺乏信息互补性，导致色彩失真和伪影。为此，本文从物理信号层面设计了一种互补融合模型。首先，为区分噪声与有效信息，我们采用双层权重引导滤波和引导滤波分别获取纹理层和边缘层；其次，通过扩展DoG滤波器对可见光与近红外的差异图进行滤波，生成初始可见光-近红外互补权重图；随后，利用反正切函数处理近红外夜间补偿显著区域，对初始互补权重图进行修正；最后，分别通过可见光与近红外图像的互补权重图生成融合图像。实验结果表明，所提算法不仅能充分利用光谱特性与信息互补性，还可避免色彩不自然现象且保持图像自然度，其性能优于现有最先进方法。