  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.
