In this paper we address the problem of deconvolution of an image corrupted with Poisson noise by reformulating the restoration process as a constrained minimization of a suitable regularized data fidelity function. The minimization step is performed by means of an interior-point approach, in which the constraints are incorporated within the objective function through a barrier penalty and a forward-backward algorithm is exploited to build a minimizing sequence. The key point of our proposed scheme is that the choice of the regularization, barrier and step-size parameters defining the interior point approach is automatically performed by a deep learning strategy. Numerical tests on Poisson corrupted benchmark datasets show that our method can obtain very good performance when compared to a state-of-the-art variational deblurring strategy.

A hybrid interior point - Deep learning approach for poisson image deblurring / Galinier, M.; Prato, M.; Chouzenoux, E.; Pesquet, J. -C.. - 2020:(2020), pp. 1-6. (Intervento presentato al convegno 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 tenutosi a fin nel 2020) [10.1109/MLSP49062.2020.9231876].

A hybrid interior point - Deep learning approach for poisson image deblurring

Galinier M.;Prato M.;
2020

Abstract

In this paper we address the problem of deconvolution of an image corrupted with Poisson noise by reformulating the restoration process as a constrained minimization of a suitable regularized data fidelity function. The minimization step is performed by means of an interior-point approach, in which the constraints are incorporated within the objective function through a barrier penalty and a forward-backward algorithm is exploited to build a minimizing sequence. The key point of our proposed scheme is that the choice of the regularization, barrier and step-size parameters defining the interior point approach is automatically performed by a deep learning strategy. Numerical tests on Poisson corrupted benchmark datasets show that our method can obtain very good performance when compared to a state-of-the-art variational deblurring strategy.
2020
30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
fin
2020
2020
1
6
Galinier, M.; Prato, M.; Chouzenoux, E.; Pesquet, J. -C.
A hybrid interior point - Deep learning approach for poisson image deblurring / Galinier, M.; Prato, M.; Chouzenoux, E.; Pesquet, J. -C.. - 2020:(2020), pp. 1-6. (Intervento presentato al convegno 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 tenutosi a fin nel 2020) [10.1109/MLSP49062.2020.9231876].
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1223563
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact