u-ist
Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
Gharbi, Mouna, Villa, Silvia, Chouzenoux, Emilie, Pesquet, Jean-Christophe, Duval, Laurent
Data restoration from degraded observations, of sparsity hypotheses, is an active field of study. Traditional iterative optimization methods are now complemented by deep learning techniques. The development of unfolded methods benefits from both families. We carry out a comparative study of three architectures on parameterized chromatographic signal databases, highlighting the performance of these approaches, especially when employing metrics adapted to physico-chemical peak signal characterization.
2510.1876
Country:
- Europe > Austria > Styria > Graz (0.05)
- North America > United States > Maine (0.04)
- Europe > Italy (0.04)
- Europe > France > Île-de-France > Hauts-de-Seine > Rueil-Malmaison (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)