From linear to nonlinear unfolded Condat-Vũ algorithm for spectro-polarimetric hight-constrast image recovery

E Chappon; N Pustelnik; J Tachella; L Denneulin; A Ferrari; M Langlois

Preprints, Working Papers, ... Year : 2024

From linear to nonlinear unfolded Condat-Vũ algorithm for spectro-polarimetric hight-constrast image recovery

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E Chappon

Function : Author

Laboratoire de Physique de l'ENS Lyon

N Pustelnik

Function : Author

Laboratoire de Physique de l'ENS Lyon

J Tachella

Function : Author

Laboratoire de Physique de l'ENS Lyon

L Denneulin

Function : Author

Ecole Pour l'Informatique et les Techniques Avancées

A Ferrari

Function : Author

Observatoire de la Côte d'Azur

M Langlois

Function : Author

Centre de Recherche Astrophysique de Lyon

Keywords

Unfolded proximal neural network high constrast image recovery deep learning

Domains

Signal and Image processing

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rhapsodie_PNN.pdf (2.45 Mo)

Origin : Files produced by the author(s)

Nelly Pustelnik : Connect in order to contact the contributor

https://hal.science/hal-04540508

Submitted on : Thursday, April 11, 2024-8:08:44 PM

Last modification on : Saturday, April 13, 2024-3:19:32 AM

Dates and versions

hal-04540508 , version 1 (11-04-2024)

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HAL Id : hal-04540508 , version 1

Cite

E Chappon, N Pustelnik, J Tachella, L Denneulin, A Ferrari, et al.. From linear to nonlinear unfolded Condat-Vũ algorithm for spectro-polarimetric hight-constrast image recovery. 2024. ⟨hal-04540508⟩

From linear to nonlinear unfolded Condat-Vũ algorithm for spectro-polarimetric hight-constrast image recovery

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