  Deep learning algorithms have demonstrated state-of-the-art performance in
various tasks of image restoration. This was made possible through the ability
of CNNs to learn from large exemplar sets. However, the latter becomes an issue
for hyperspectral image processing where datasets commonly consist of just a
few images. In this work, we propose a new approach to denoising, inpainting,
and super-resolution of hyperspectral image data using intrinsic properties of
a CNN without any training. The performance of the given algorithm is shown to
be comparable to the performance of trained networks, while its application is
not restricted by the availability of training data. This work is an extension
of original "deep prior" algorithm to HSI domain and 3D-convolutional networks.
