Nuclear magnetic resonance (NMR) is one of the most powerful analytical tools and is extensively applied in many fields. However, compared to other spectroscopic techniques, NMR has lower sensitivity, impeding its wider applications. Using data postprocessing techniques to increase the NMR spectral signal-to-noise ratio (SNR) is a relatively simple and cost-effective method. In this work, a deep neural network, termed as DN-Unet, is devised to suppress noise in liquid-state NMR spectra to enhance SNR. It combines structures of encoder–decoder and convolutional neural network. Different from traditional deep learning training strategy, M-to-S strategy is developed to enhance DN-Unet capability that multiple noisy spectra (inputs) correspond to a same single noiseless spectrum (label) in the training stage. The trained 1D model can be used for denoising not only 1D but also high dimension spectra, further improving DN-Unet’s performance. 1D, 2D, and 3D NMR spectra were utilized to evaluate DN-Unet performance. The results suggest that DN-Unet provides larger than 200-fold increase in SNR with weak peaks hidden in noise perfectly recovered and spurious peaks suppressed well. Since DN-Unet developed here to increase SNR is based on data postprocessing, it is universal for a variety of samples and NMR platforms. The great SNR enhancement and extreme excellence in differentiating signal and noise would greatly promote various liquid-state NMR applications.

中文翻译：

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通过深层神经网络DN-Unet改善液相NMR光谱的信噪比

核磁共振（NMR）是最强大的分析工具之一，已广泛应用于许多领域。但是，与其他光谱技术相比，NMR具有较低的灵敏度，从而阻碍了其更广泛的应用。使用数据后处理技术来增加NMR光谱信噪比（SNR）是一种相对简单且具有成本效益的方法。在这项工作中，设计了称为DN-Unet的深度神经网络来抑制液态NMR光谱中的噪声以增强SNR。它结合了编码器-解码器和卷积神经网络的结构。与传统的深度学习训练策略不同，开发了M-to-S策略以增强DN-Unet功能，即在训练阶段，多个噪声频谱（输入）对应于同一单个无噪声频谱（标签）。经过训练的一维模型不仅可以用于一维去噪，还可以用于高维频谱去噪，从而进一步改善了DN-Unet的性能。1D，2D和3D NMR光谱用于评估DN-Unet性能。结果表明，DN-Unet的SNR增大了200倍以上，隐藏在噪声中的弱峰得以完美恢复，杂散峰得到了很好的抑制。由于此处开发用于提高SNR的DN-Unet是基于数据后处理的，因此它适用于各种样品和NMR平台。巨大的SNR增强以及在区分信号和噪声方面的卓越表现将极大地促进各种液态NMR应用。结果表明，DN-Unet的SNR增大了200倍以上，隐藏在噪声中的弱峰得以完美恢复，杂散峰得到了很好的抑制。由于此处开发用于提高SNR的DN-Unet是基于数据后处理的，因此它适用于各种样品和NMR平台。巨大的SNR增强以及在区分信号和噪声方面的卓越表现将极大地促进各种液态NMR应用。结果表明，DN-Unet的SNR增大了200倍以上，隐藏在噪声中的弱峰得以完美恢复，杂散峰得到了很好的抑制。由于此处开发用于提高SNR的DN-Unet是基于数据后处理的，因此它适用于各种样品和NMR平台。巨大的SNR增强以及在区分信号和噪声方面的卓越表现将极大地促进各种液态NMR应用。