A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy

TL;DR

This paper introduces a lightweight, reproducible Noise2Noise-based denoising pipeline for high-throughput Raman spectroscopy, enabling fast spectral acquisition with high fidelity without external references.

Contribution

The work presents a novel autoencoder-based denoising method that does not require spectral libraries or high SNR references, suitable for rapid Raman data processing.

Findings

Denoised spectra maintain high fidelity at 5 ms acquisition times.

The pipeline improves spectral quality without external spectral references.

It enables faster Raman spectroscopy workflows with preserved chemical information.

Abstract

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.