This work proposes a denoising protocol, namely CCA-NMR, that exploits the idea of blind source separation to separate noise and real signals based on autocorrelation coefficients, delivering good noise reduction, fast computational time, and decent universality in NMR spectroscopy.
Haolin Zhan, Yueyao Li, Yong-Qi Yuan et al.· Chemical Science· 0 citations
STELT (Spatiotemporal Extraction Laplace Transform), a lightweight deep-learning framework based on spatiotemporal feature extraction, is proposed, which achieves superior reconstruction accuracy and noise suppression with significantly reduced computational overhead.
Jing-Min Lin, Bo Chen, Guolan Peng et al.· Journal of Chemical Physics· 0 citations
An accelerated conditional diffusion model for multidimensional NMR spectra reconstruction is presented, formulating the task as a probabilistic iterative denoising process that progressively refines undersampled spectra under physical constraints and outperforms both traditional and end-to-end deep learning algorithms...
Bo Chen, Xun Guan, Zhuoran Rong et al.· National Science Review· 0 citations
This study presents a diffusion-based deep-learning framework for automatic phase-sensitive NMR spectrum reconstruction directly from common NMR experimental data, free of quadrature acquisition and phase correction operation.
Zhuoran Rong, Bo Chen, Jie Shao et al.· Analytical Chemistry· 0 citations
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