Neural network enabled time stretch spectral regression

Spectral interferometry is utilized in a wide range of biomedical and scientific applications and metrology. Retrieving the magnitude and phase of the complex electric field from the interferogram is central to all its applications. We report a spectral interferometry system that utilizes a neural network to infer the magnitude and phase of femtosecond interferograms directly from the measured single-shot interference patterns and compare its performance with the widely used Hilbert transform. Our approach does not require apriori knowledge of the shear frequency, and achieves higher accuracy under our experimental conditions. To train the network, we introduce an experimental technique that generates a large number of femtosecond interferograms with known (labeled) phase and magnitude profiles. While the profiles for these pulses are digitally generated, they obey causality by satisfying the Kramer-Kronig relation. This technique is resilient against nonlinear optical distortions, quantization noise, and the sampling rate limit of the backend digitizer - valuable properties that relax instrument complexity and cost. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

Spectral interferometry is widely used in biomedical and scientific applications, and a system utilizing a neural network to directly infer the magnitude and phase of femtosecond interferograms without prior knowledge achieves higher accuracy under experimental conditions. This approach introduces a technique to train the network using a large number of labeled interferograms generated with known phase and magnitude profiles, demonstrating resilience against various optical distortions and noise.