Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911224516575232 |
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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 |