TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy
This work addresses noise reduction in NMR spectroscopy for fields like bio-medicine and chemistry, but it is incremental as it builds on existing deep learning and traditional methods.
The paper tackled the problem of low signal-to-noise ratio in NMR spectroscopy by proposing TVCondNet, a method that combines deep learning with traditional total variation denoising, resulting in superior denoising performance and faster inference speed on experimentally collected data.
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signals from NMR spectroscopy often have low signal-to-noise ratio (SNR) due to acquisition noise, which poses significant challenges for subsequent analysis. Recent work has explored the potential of deep learning (DL) for NMR denoising, showing significant performance gains over traditional methods such as total variation (TV) denoising. This paper shows that the performance of DL denoising for NMR can be further improved by combining data-driven training with traditional TV denoising. The proposed TVCondNet method outperforms both traditional TV and DL methods by including the TV solution as a condition during DL training. Our validation on experimentally collected NMR data shows the superior denoising performance and faster inference speed of TVCondNet compared to existing methods.