  This paper proposes a non-data-driven deep neural network for spectral image
recovery problems such as denoising, single hyperspectral image
super-resolution, and compressive spectral imaging reconstruction. Unlike
previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns
the prior information through the network. Mixture-Net consists of a deep
generative model whose layers are inspired by the linear and non-linear
low-rank mixture models, where the recovered image is composed of a weighted
sum between the linear and non-linear decomposition. Mixture-Net also provides
a low-rank decomposition interpreted as the spectral image abundances and
endmembers, helpful in achieving remote sensing tasks without running
additional routines. The experiments show the MixtureNet effectiveness
outperforming state-of-the-art methods in recovery quality with the advantage
of architecture interpretability.
