Improving signal-to-noise ratio of a terahertz signal using a WaveNet-based neural network

When acquiring a terahertz signal from a time-domain spectroscopy system, the signal is degraded by measurement noise and the information embedded in the signal is distorted. For high-performing terahertz applications, this study proposes a method for enhancing such a noise-degraded terahertz signal using machine learning that is applied to the raw signal after acquisition. The proposed method learns a function that maps the degraded signal to the clean signal using a WaveNet-based neural network that performs multiple layers of dilated convolutions. It also includes learnable pre- and post-processing modules that automatically transform the time domain where the enhancement process operates. When training the neural network, a data augmentation scheme is adopted to tackle the issue of insufficient training data. The comparative evaluation confirms that the proposed method outperforms other baseline neural networks in terms of signal-to-noise ratio. The proposed method also performs significantly better than the averaging of multiple signals, thereby facilitating the procurement of an enhanced signal without increasing the measurement time. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

A method for enhancing noise-degraded terahertz signals using machine learning is proposed, which learns a function through a neural network to map degraded signals to clean signals, outperforming other methods in terms of signal-to-noise ratio.