# A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy

arXiv: https://arxiv.org/abs/2605.18511 · HTML: https://mlsanity.com/paper/2605.18511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 3 of 3)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [High-throughput molecular imaging via deep learning enabled Raman spectroscopy](https://mlsanity.com/paper/2009.13318/) (2020)
- [Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopy](https://mlsanity.com/paper/2509.25964/) (2025)

## Abstract (arXiv)

A lightweight and reproducible denoising pipeline for high-throughput Raman spectroscopy is presented. The approach relies on a one-dimensional convolutional autoencoder trained using a Noise2Noise strategy, requiring neither external spectral libraries nor high signal-to-noise reference spectra for training. From a reduced training subset composed of repeated short-exposure acquisitions, the model learns to reconstruct Raman spectra while efficiently suppressing stochastic noise. The method is evaluated on a heterogeneous mineral sample, using both quantitative spectral fidelity metrics (RMSE, SNR, SSIM) and task-oriented criteria based on unsupervised K-means classification. Results demonstrate that integration times as short as 5 ms per spectrum, which are typically insufficient for reliable interpretation, yield denoised spectra with high fidelity to the reference data while preserving chemically coherent maps. This work provides a practical trade-off between spectral quality and acquisition speed, enabling fast, adaptable Raman workflows compatible with routine laboratory use. It also offers a transferable framework for other one-dimensional spectroscopic modalities.