  Multispectral imaging is an important task of image processing and computer
vision, which is especially relevant to applications such as dehazing or object
detection. With the development of the RGBT (RGB & Thermal) sensor, the problem
of visible (RGB) to Near Infrared (NIR) image fusion has become particularly
timely. Indeed, while visible images see color, but suffer from noise, haze,
and clouds, the NIR channel captures a clearer picture. The proposed approach
fuses these two channels by training a Convolutional Neural Network by Self
Supervised Learning (SSL) on a single example. For each such pair, RGB and NIR,
the network is trained for seconds to deduce the final fusion. The SSL is based
on the comparison of the Structure of Similarity and Edge-Preservation losses,
where the labels for the SSL are the input channels themselves. This fusion
preserves the relevant detail of each spectral channel without relying on a
heavy training process. Experiments demonstrate that the proposed approach
achieves similar or better qualitative and quantitative multispectral fusion
results than other state-of-the-art methods that do not rely on heavy training
and/or large datasets.
