Deep learning-assisted wavefront correction with sparse data for holographic tomography
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Publication Date
2022Journal Title
Optics and Lasers in Engineering
ISSN
0143-8166
Publisher
Elsevier BV
Type
Article
This Version
AM
Metadata
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Lin, L., Huang, C., Chen, Y., Chu, D., & Cheng, C. (2022). Deep learning-assisted wavefront correction with sparse data for holographic tomography. Optics and Lasers in Engineering https://doi.org/10.1016/j.optlaseng.2022.107010
Abstract
In this paper, a novel approach using deep learning-assisted wavefront correction
in beam rotation holographic tomography to acquire three-dimensional images of native
biological cell samples is described. With digitally recorded holograms, the wavefront
aberration is contained in the reconstructed phases. However, there are large
computation costs for compensating the phase aberration during the reconstruction.
With the aid of a deep convolution network, we present an effective algorithm on the
reconstructed phases with sparse data for active wavefront correction. To accomplish
this, we developed a Res-Unet scheme to segment the cell region from its background
aberration and a deep regression network for the representation of the aberration on
Zernike orthonormal basis. Moreover, a sparse data fitting algorithm was used to
predict the Zernike coefficients of whole scanning angles from the collected sparse data.
As a result, the proposed algorithm is capable of accurately correcting the background
aberration and much faster than the original plain algorithm.
Sponsorship
This work is financially supported by the Ministry of Science and Technology, Taiwan: MOST 109-
2221-E-035-074, MOST 108-2221-E-003-019-MY3, MOST 107-2923-E-003-001-MY3, and MOST
110-2218-E-011009-MBK.
Embargo Lift Date
2023-03-05
Identifiers
External DOI: https://doi.org/10.1016/j.optlaseng.2022.107010
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334386
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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