From Linear to Nonlinear Unfolded Condat-Vú Algorithm for Spectro-Polarimetric Hight-Constrast Image Recovery
Year: 2024
Type: conference-paper
Abstract: Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat- Vii iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.
Institutions: École Normale Supérieure de Lyon, Centre National de la Recherche Scientifique, Laboratoire de Physique de l'ENS de Lyon, École Pour l'Informatique et les Techniques Avancées, Lagrange Laboratory, Observatoire de la Côte d’Azur, Lyon 1 Université, Centre de Recherche Astrophysique de Lyon
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4394737621