Deep learning as phase retrieval tool for CARS spectra

Houhou R, Barman P, Schmitt M, Meyer T, Popp J, Bocklitz T (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Optics Express Optical Society of America

Book Volume: 28

Pages Range: 21002-21024

Journal Issue: 14

DOI: 10.1364/OE.390413

Abstract

Finding efficient and reliable methods for the extraction of the phase in optical measurements is challenging and has been widely investigated. Although sophisticated optical settings, e.g. holography, measure directly the phase, the use of algorithmic methods has gained attention due to its efficiency, fast calculation and easy setup requirements. We investigated three phase retrieval methods: the maximum entropy technique (MEM), the Kramers-Kronig relation (KK), and for the first time deep learning using the Long Short-Term Memory network (LSTM). LSTM shows superior results for the phase retrieval problem of coherent anti-Stokes Raman spectra in comparison to MEM and KK.

Involved external institutions

Friedrich-Schiller-Universität Jena DE Germany (DE) Leibniz-Institut für Photonische Technologien e.V. DE Germany (DE)

How to cite

APA:

Houhou, R., Barman, P., Schmitt, M., Meyer, T., Popp, J., & Bocklitz, T. (2020). Deep learning as phase retrieval tool for CARS spectra. Optics Express, 28(14), 21002-21024. https://doi.org/10.1364/OE.390413

MLA:

Houhou, Rola, et al. "Deep learning as phase retrieval tool for CARS spectra." Optics Express 28.14 (2020): 21002-21024.

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