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

TL;DR

This paper compares three unfolded deep learning architectures for restoring sparse chromatographic signals, emphasizing their performance with physico-chemical peak metrics, and highlights the benefits of combining traditional and deep learning methods.

Contribution

It provides a comparative analysis of unfolded architectures for chromatographic signal restoration, integrating traditional optimization and deep learning approaches.

Findings

Unfolded methods outperform traditional iterative algorithms.

Performance varies depending on physico-chemical metrics used.

Deep learning architectures show promising results in sparse signal restoration.

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.