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arXiv · 2510.18760

Analyse comparative d'algorithmes de restauration en architecture d\'epli\'ee pour des signaux chromatographiques parcimonieux

Mouna Gharbi·Silvia Villa·Emilie Chouzenoux·Jean-Christophe Pesquet·Laurent Duval
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Abstract

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.

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Mouna Gharbi, Silvia Villa, Emilie Chouzenoux, Jean-Christophe Pesquet, Laurent Duval. 2025-10-01. Analyse comparative d'algorithmes de restauration en architecture d\'epli\'ee pour des signaux chromatographiques parcimonieux. https://arxiv.org/abs/2510.18760

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