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Abstract：
Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation.
摘要：
预处理在拉曼光谱分析中起着关键作用。然而,经典的预处理算法通常具有在处理光谱时降低拉曼峰强度和改变峰形状的问题。本文介绍了一种基于卷积自动编码器的统一预处理解决方案，以增强拉曼光谱数据。一种是使用卷积去噪自动编码器（CDAE模型）的去噪算法，另一种是基于卷积自动编码器(CAE+模型)的基线校正算法。CDAE模型在其瓶颈层中包含两个额外的卷积层，以增强降噪效果。CAE+模型不仅在瓶颈处添加卷积层,而且在解码之后包括用于有效基线校正的比较函数。使用拉曼光谱仪系统测量的模拟光谱和实验光谱对所提出的模型进行了验证。将它们的性能与传统信号处理技术的性能进行比较，CDAE-CAE+模型的结果表明在降噪和拉曼峰保存方面有改善。
