# TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy

arXiv: https://arxiv.org/abs/2405.11064 · HTML: https://mlsanity.com/paper/2405.11064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy](https://mlsanity.com/paper/2001.04813/) (2020)
- [Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning](https://mlsanity.com/paper/1904.05168/) (2019)
- [Hankel Matrix Nuclear Norm Regularized Tensor Completion for N-dimensional Exponential Signals](https://mlsanity.com/paper/1604.02100/) (2016)
- [Review and Prospect: NMR Spectroscopy Denoising & Reconstruction with Low Rank Hankel Matrices and Tensors](https://mlsanity.com/paper/2007.12646/) (2020)
- [Denoising neural networks for magnetic resonance spectroscopy](https://mlsanity.com/paper/2211.00080/) (2022)
- [NMR Spectra Denoising with Vandermonde Constraints](https://mlsanity.com/paper/2310.13882/) (2023)

## Built on by (most cited first, 1 of 1)

- [Adaptive double-phase Rudin--Osher--Fatemi denoising model](https://mlsanity.com/paper/2510.04382/) (2025)

## Abstract (arXiv)

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.