• Corpus ID: 245144739

Supervised learning of analysis-sparsity priors with automatic differentiation

@inproceedings{Ghanem2021SupervisedLO,
  title={Supervised learning of analysis-sparsity priors with automatic differentiation},
  author={Hashem Ghanem and Joseph Salmon and Nicolas Keriven and Samuel Vaiter},
  year={2021}
}
Sparsity priors are commonly used in denoising and image reconstruction. For analysis-type priors, a dictionary defines a representation of signals that is likely to be sparse. In most situations, this dictionary is not known, and is to be recovered from pairs of ground-truth signals and measurements, by minimizing the reconstruction error. This defines a hierarchical optimization problem, which can be cast as a bi-level optimization. Yet, this problem is unsolvable, as reconstructions and… 
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