  Hyperspectral imaging offers new perspectives for diverse applications,
ranging from the monitoring of the environment using airborne or satellite
remote sensing, precision farming, food safety, planetary exploration, or
astrophysics. Unfortunately, the spectral diversity of information comes at the
expense of various sources of degradation, and the lack of accurate
ground-truth "clean" hyperspectral signals acquired on the spot makes
restoration tasks challenging. In particular, training deep neural networks for
restoration is difficult, in contrast to traditional RGB imaging problems where
deep models tend to shine. In this paper, we advocate instead for a hybrid
approach based on sparse coding principles that retains the interpretability of
classical techniques encoding domain knowledge with handcrafted image priors,
while allowing to train model parameters end-to-end without massive amounts of
data. We show on various denoising benchmarks that our method is
computationally efficient and significantly outperforms the state of the art.
