  Cross spectral camera arrays, where each camera records different spectral
content, are becoming increasingly popular for RGB, multispectral and
hyperspectral imaging, since they are capable of a high resolution in every
dimension using off-the-shelf hardware. For these, it is necessary to build an
image processing pipeline to calculate a consistent image data cube, i.e., it
should look like as if every camera records the scene from the center camera.
Since the cameras record the scene from a different angle, this pipeline needs
a reconstruction component for pixels that are not visible to peripheral
cameras. For that, a novel deep guided neural network (DGNet) is presented.
Since only little cross spectral data is available for training, this neural
network is highly regularized. Furthermore, a new data augmentation process is
introduced to generate the cross spectral content. On synthetic and real
multispectral camera array data, the proposed network outperforms the state of
the art by up to 2 dB in terms of PSNR on average. Besides, DGNet also tops its
best competitor in terms of SSIM as well as in runtime by a factor of nearly
12. Moreover, a qualitative evaluation reveals visually more appealing results
for real camera array data.
