A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is presented. The approach relies on a 1D 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 1D spectroscopic modalities.