A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy
A new denoising pipeline for high-throughput Raman spectroscopy has been developed. This method utilizes a one-dimensional convolutional autoencoder trained with a Noise2Noise strategy, allowing for effective noise suppression without the need for external spectral libraries. The results indicate that even short acquisition times can yield high-fidelity denoised spectra, enhancing the practicality of Raman workflows in laboratory settings.
- ▪The denoising pipeline is lightweight and reproducible.
- ▪It does not require high signal-to-noise reference spectra for training.
- ▪The method was evaluated on a heterogeneous mineral sample using quantitative spectral fidelity metrics.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18511 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.18511 (cs) [Submitted on 18 May 2026] Title:A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy Authors:David Martin-Calle (ILM,UCBL,CNRS), Cesar Alvarez Llamas (ILM,UCBL,CNRS), Vincent Motto- Ros (ILM,UCBL,CNRS), Christophe Dujardin (ILM,UCBL,CNRS,IUF), Jérémie Margueritat (ILM,UCBL,CNRS), David Rodney (ILM,UCBL,CNRS) View a PDF of the paper titled A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy, by David Martin-Calle (ILM and 18 other authors View PDF Abstract:A lightweight and reproducible denoising pipeline for high-throughput Raman spectroscopy is presented.
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