  Multispectral imaging aims at recording images in different spectral bands.
This is extremely beneficial in diverse discrimination applications, for
example in agriculture, recycling or healthcare. One approach for snapshot
multispectral imaging, which is capable of recording multispectral videos, is
by using camera arrays, where each camera records a different spectral band.
Since the cameras are at different spatial positions, a registration procedure
is necessary to map every camera to the same view. In this paper, we present a
multispectral snapshot image registration with three novel components. First, a
cross spectral disparity estimation network is introduced, which is trained on
a popular stereo database using pseudo spectral data augmentation.
Subsequently, this disparity estimation is used to accurately detect occlusions
by warping the disparity map in a layer-wise manner. Finally, these detected
occlusions are reconstructed by a learned deep guided neural network, which
leverages the structure from other spectral components. It is shown that each
element of this registration process as well as the final result is superior to
the current state of the art. In terms of PSNR, our registration achieves an
improvement of over 3 dB. At the same time, the runtime is decreased by a
factor of over 3 on a CPU. Additionally, the registration is executable on a
GPU, where the runtime can be decreased by a factor of 111. The source code and
the data is available at https://github.com/FAU-LMS/MSIR.
