  In many scientific applications, measured time series are corrupted by noise
or distortions. Traditional denoising techniques often fail to recover the
signal of interest, particularly when the signal-to-noise ratio is low or when
certain assumptions on the signal and noise are violated. In this work, we
demonstrate that deep learning-based denoising methods can outperform
traditional techniques while exhibiting greater robustness to variation in
noise and signal characteristics. Our motivating example is magnetic resonance
spectroscopy, in which a primary goal is to detect the presence of
short-duration, low-amplitude radio frequency signals that are often obscured
by strong interference that can be difficult to separate from the signal using
traditional methods. We explore various deep learning architecture choices to
capture the inherently complex-valued nature of magnetic resonance signals. On
both synthetic and experimental data, we show that our deep learning-based
approaches can exceed performance of traditional techniques, providing a
powerful new class of methods for analysis of scientific time series data.
