Abstract Raman spectroscopy is a non-destructive testing technology that has been widely used in biomedicine and other fields. However, the spontaneous Raman signal is weak. Therefore, the data collection is very time consuming, which has seriously hindered the application of Raman imaging technology in the fast dynamic system. At present, the reconstruction of Raman spectra based on multi-channel measurements is an effective method to solve this problem. However, the limitation of this algorithm is that the substance to be tested is known. In this paper, we have completed a new algorithm to solve this problem. Firstly, the feature abstraction of data is obtained based on the principal component analysis (PCA). Combining with the support vector machine (SVM), a spectral recognition algorithm is established. Secondly, the partial least squares and BP neural network are used to complete three algorithms for the reconstruction, which is based on the finite-dimensional model. Finally, the full Raman spectra and image are rapidly reconstructed. In the experiment, the Polymethyl Methacrylate (PMMA) is selected as the experimental sample. Meanwhile, the normalization algorithm and the polynomial regression are also introduced to complete the reconstruction of Raman spectrum. Based on the root mean square error (RMSE) and imaging time, the performance of the proposed algorithm is evaluated. The results show that the reconstructed spectrum by our algorithm in this paper is better than the traditional algorithms. Meanwhile, the imaging time is much lower than the traditional imaging modes. Therefore, this algorithm provides a theoretical support for the application of Raman imaging technology in fast dynamic systems.

中文翻译：

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基于优化的多通道成像系统有限维模型的拉曼光谱和图像快速重建

摘要 拉曼光谱是一种无损检测技术，已广泛应用于生物医学等领域。然而，自发拉曼信号很弱。因此，数据采集非常耗时，严重阻碍了拉曼成像技术在快速动态系统中的应用。目前，基于多通道测量的拉曼光谱重建是解决这一问题的有效方法。但是，该算法的局限性在于要测试的物质是已知的。在本文中，我们完成了一个新的算法来解决这个问题。首先，基于主成分分析（PCA）获得数据的特征抽象。结合支持向量机（SVM），建立了光谱识别算法。第二，采用偏最小二乘法和BP神经网络完成三种基于有限维模型的重构算法。最后，快速重建完整的拉曼光谱和图像。实验中选择聚甲基丙烯酸甲酯（PMMA）作为实验样品。同时，还引入归一化算法和多项式回归来完成拉曼光谱的重建。基于均方根误差（RMSE）和成像时间，对该算法的性能进行了评估。结果表明，本文算法重建的频谱优于传统算法。同时，成像时间远低于传统成像模式。所以，