Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
This work addresses signal restoration for chromatographic data, which is incremental as it compares existing unfolded methods on a specific domain.
The paper conducted a comparative study of three unfolded deep learning architectures for restoring sparse chromatographic signals from degraded data, finding that these approaches performed well, particularly when using metrics tailored to physico-chemical peak characterization.
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.