用噪声学噪声，5毫秒采样也能出高质拉曼谱
A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy
- 用一维卷积自编码器，仅靠重复短曝光数据训练去噪
- 5毫秒/谱的极短采集时间，仍能还原接近参考谱的精度
- 无需参考数据，适合实验室快速拉曼分析与迁移应用
提出一种轻量且可复现的高通量拉曼光谱去噪流程。该方法基于一维卷积自编码器，采用Noise2Noise策略训练，无需外部光谱库或高信噪比参考谱。仅需由多次短曝光采集组成的少量训练样本，模型即可学习重建拉曼谱并有效抑制随机噪声。在异质矿物样品上评估，采用定量谱保真度指标（RMSE、SNR、SSIM）和基于无监督K-means分类的任务导向标准。结果表明，每谱仅5毫秒的极短积分时间，虽通常不足以可靠解读，经去噪后仍能获得与参考数据高度一致的谱图，并保持化学信息一致的分布图。该工作实现了谱质与采集速度间的实用平衡，支持快速、可适配的拉曼实验流程，适用于常规实验室使用，亦可迁移至其他一维光谱模态。
原文摘要 · Abstract (English)
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.
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