Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MLSP49062.2020.9231876 | 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Interior point method,proximal algorithms,deep unfolding,neural network,Poisson image restoration | Conference | 1551-2541 |
ISBN | Citations | PageRank |
978-1-7281-6663-6 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathilde Galinier | 1 | 0 | 0.34 |
| Marco Prato | 2 | 0 | 0.34 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |
| Jean-Christophe Pesquet | 4 | 18 | 11.52 |