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Main Authors: Gharbi, Mouna, Villa, Silvia, Chouzenoux, Emilie, Pesquet, Jean-Christophe, Duval, Laurent
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2510.18760
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author Gharbi, Mouna
Villa, Silvia
Chouzenoux, Emilie
Pesquet, Jean-Christophe
Duval, Laurent
author_facet Gharbi, Mouna
Villa, Silvia
Chouzenoux, Emilie
Pesquet, Jean-Christophe
Duval, Laurent
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2510_18760
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle 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
Signal Processing
Machine Learning
Chemical Physics
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.
title Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
topic Signal Processing
Machine Learning
Chemical Physics
url https://arxiv.org/abs/2510.18760