We report the potential of U‐Net deep neural network for the efficient removal of noise and background from raw Raman spectra. The U‐Net method was first trained on simulated spectra and then tested with experimental spectra. The quality of the test results was quantified via different signal‐to‐noise ratios and the structural similarity index metric. The U‐Net recovered Raman spectra feature a high structural similarity index, even for raw spectra that were dominated by background. The U‐Net model does not rely on any human intervention.

中文翻译：

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使用深层神经网络细化光谱：全自动去除噪音和背景

我们报告了U-Net深度神经网络从原始拉曼光谱中有效去除噪声和背景的潜力。U-Net方法首先在模拟光谱上训练，然后在实验光谱上进行测试。测试结果的质量通过不同的信噪比和结构相似性指标进行量化。U-Net回收的拉曼光谱具有很高的结构相似性指数，即使对于以背景为主的原始光谱也是如此。U-Net模型不依赖任何人工干预。