  Multispectral image fusion is a computer vision process that is essential to
remote sensing. For applications such as dehazing and object detection, there
is a need to offer solutions that can perform in real-time on any type of
scene. Unfortunately, current state-of-the-art approaches do not meet these
criteria as they need to be trained on domain-specific data and have high
computational complexity. This paper focuses on the task of fusing color (RGB)
and near-infrared (NIR) images as this the typical RGBT sensors, as in
multispectral cameras for detection, fusion, and dehazing. Indeed, the NIR
channel has the ability to capture details not visible in RGB and see beyond
haze, fog, and clouds. To combine this information, a novel approach based on
superpixel segmentation is designed so that multispectral image fusion is
performed according to the specific local content of the images to be fused.
Therefore, the proposed method produces a fusion that contains the most
relevant content of each spectrum. The experiments reported in this manuscript
show that the novel approach better preserve details than alternative fusion
methods.
