Hyperspectral compressive wavefront sensing
Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its paramete...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-01-01
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| Series: | High Power Laser Science and Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2095471922000354/type/journal_article |
| _version_ | 1827313421535674368 |
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| author | Sunny Howard Jannik Esslinger Robin H. W. Wang Peter Norreys Andreas Döpp |
| author_facet | Sunny Howard Jannik Esslinger Robin H. W. Wang Peter Norreys Andreas Döpp |
| author_sort | Sunny Howard |
| collection | DOAJ |
| description | Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging. |
| first_indexed | 2024-04-24T22:08:16Z |
| format | Article |
| id | doaj.art-952fed9e7f1e42f5bfb418c7af51c5bc |
| institution | Directory Open Access Journal |
| issn | 2095-4719 2052-3289 |
| language | English |
| last_indexed | 2024-04-24T22:08:16Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | High Power Laser Science and Engineering |
| spelling | doaj.art-952fed9e7f1e42f5bfb418c7af51c5bc2024-03-20T10:48:12ZengCambridge University PressHigh Power Laser Science and Engineering2095-47192052-32892023-01-011110.1017/hpl.2022.35Hyperspectral compressive wavefront sensingSunny Howard0https://orcid.org/0000-0002-8332-964XJannik Esslinger1https://orcid.org/0000-0003-2910-1887Robin H. W. Wang2Peter Norreys3Andreas Döpp4https://orcid.org/0000-0003-2913-5729Department of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK Centre for Advanced Laser Applications, Ludwig-Maximilians-Universität München, Garching, GermanyCentre for Advanced Laser Applications, Ludwig-Maximilians-Universität München, Garching, GermanyDepartment of Physics, Clarendon Laboratory, University of Oxford, Oxford, UKDepartment of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK John Adams Institute for Accelerator Science, Oxford, UKDepartment of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK Centre for Advanced Laser Applications, Ludwig-Maximilians-Universität München, Garching, GermanyPresented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.https://www.cambridge.org/core/product/identifier/S2095471922000354/type/journal_articleartificial neural networkscompressed sensinghigh-power laser characterizationwavefront measurement |
| spellingShingle | Sunny Howard Jannik Esslinger Robin H. W. Wang Peter Norreys Andreas Döpp Hyperspectral compressive wavefront sensing High Power Laser Science and Engineering artificial neural networks compressed sensing high-power laser characterization wavefront measurement |
| title | Hyperspectral compressive wavefront sensing |
| title_full | Hyperspectral compressive wavefront sensing |
| title_fullStr | Hyperspectral compressive wavefront sensing |
| title_full_unstemmed | Hyperspectral compressive wavefront sensing |
| title_short | Hyperspectral compressive wavefront sensing |
| title_sort | hyperspectral compressive wavefront sensing |
| topic | artificial neural networks compressed sensing high-power laser characterization wavefront measurement |
| url | https://www.cambridge.org/core/product/identifier/S2095471922000354/type/journal_article |
| work_keys_str_mv | AT sunnyhoward hyperspectralcompressivewavefrontsensing AT jannikesslinger hyperspectralcompressivewavefrontsensing AT robinhwwang hyperspectralcompressivewavefrontsensing AT peternorreys hyperspectralcompressivewavefrontsensing AT andreasdopp hyperspectralcompressivewavefrontsensing |