Adaptive Noise Estimation and Denoising with Deep Learning for NMR Spectroscopy

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications. This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the signal to preserve phase information and spectral fidelity. A core innovation of this work is the integration of a noise level estimator that predicts the noise intensity present in the input signal. This estimated noise level is then used to condition the denoiser, enabling the model to adapt dynamically to a wide range of noise scenarios. The framework was evaluated across two primary settings: (1) synthetically generated noisy spectra created by adding machine-derived noise to clean Free Induction Decay (FID) signals, and (2) real experimental spectra obtained from Bruker spectrometers under uncontrolled acquisition conditions. Quantitative metrics such as Signal-to-Noise Ratio (SNR), Peak Height-to-Noise Ratio, and Normalized Mean Square Error (NMSE) were used to assess performance. Results demonstrate that the adaptive framework consistently improves spectral clarity, outperforming traditional denoising baselines and unconditioned deep learning models. By combining noise-aware learning with complex-valued signal processing, this framework offers a robust and scalable solution for enhancing NMR spectra without increasing acquisition time. These findings contribute to the growing intersection of deep learning and spectroscopic analysis, advancing the potential for automated, high-throughput NMR applications in both research and industry.