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

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration