Neural network enabled time stretch spectral regression
| Autor: | Guoqing Pu, Bahram Jalali |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal processing
Artificial neural network business.industry Computer science Astrophysics::Instrumentation and Methods for Astrophysics Phase (waves) Physics::Optics 02 engineering and technology 021001 nanoscience & nanotechnology Interference (wave propagation) 01 natural sciences Atomic and Molecular Physics and Optics Metrology 010309 optics symbols.namesake Interferometry Optics 0103 physical sciences Femtosecond symbols Hilbert transform 0210 nano-technology business Algorithm |
| Zdroj: | Optics express. 29(13) |
| ISSN: | 1094-4087 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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