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

Théo Bodrito 1 Alexandre Zouaoui 1 Jocelyn Chanussot 1 Julien Mairal 1 
1 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : 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.
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Contributor : Théo Bodrito Connect in order to contact the contributor
Submitted on : Wednesday, November 17, 2021 - 11:31:52 AM
Last modification on : Thursday, February 3, 2022 - 11:17:47 AM
Long-term archiving on: : Friday, February 18, 2022 - 6:05:46 PM


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  • HAL Id : hal-03423559, version 1
  • ARXIV : 2111.09708



Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal. A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration. NeurIPS 2021 – 35th Annual Conference on Neural Information Processing Systems, Dec 2021, Sydney, Australia. pp.1-19. ⟨hal-03423559⟩



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