Lensless imaging of pollen grains at three-wavelengths using deep learning
Lensless imaging of pollen grains at three-wavelengths using deep learning
Image reconstruction of pollen grains was performed using neural networks, from light scattering patterns recorded with simultaneous irradiation at three laser wavelengths. The shapes of the reconstructed optical images using one network were shown to have a pixel accuracy on average of 98.9%. Two other neural networks were shown to be able to convert scattering patterns into predictions of z-stack maximum intensity projection microscope images and scanning electron microscopy images. The capability of producing magnified images in a variety of formats directly from scattering patterns will be applicable to particle sensing in a range of fields, including health and safety, environmental protection, ocean and space science.
deep learning, lensless imaging, optics, particle pollution, pollen, sensing
Grant-Jacob, James
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Praeger, Matthew
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Loxham, Matthew
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Eason, R.W.
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Mills, Benjamin
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28 July 2020
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
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Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James, Praeger, Matthew, Loxham, Matthew, Eason, R.W. and Mills, Benjamin
(2020)
Lensless imaging of pollen grains at three-wavelengths using deep learning.
Environmental Research Communications, 2 (7).
(doi:10.1088/2515-7620/aba6d1).
Abstract
Image reconstruction of pollen grains was performed using neural networks, from light scattering patterns recorded with simultaneous irradiation at three laser wavelengths. The shapes of the reconstructed optical images using one network were shown to have a pixel accuracy on average of 98.9%. Two other neural networks were shown to be able to convert scattering patterns into predictions of z-stack maximum intensity projection microscope images and scanning electron microscopy images. The capability of producing magnified images in a variety of formats directly from scattering patterns will be applicable to particle sensing in a range of fields, including health and safety, environmental protection, ocean and space science.
Text
Lensless JAGJ
- Accepted Manuscript
More information
e-pub ahead of print date: 16 July 2020
Published date: 28 July 2020
Keywords:
deep learning, lensless imaging, optics, particle pollution, pollen, sensing
Identifiers
Local EPrints ID: 442540
URI: http://eprints.soton.ac.uk/id/eprint/442540
ISSN: 2515-7620
PURE UUID: e8a19c0a-8943-49d5-b1aa-fbdb6630e095
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Date deposited: 17 Jul 2020 16:31
Last modified: 17 Mar 2024 03:35
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Contributors
Author:
James Grant-Jacob
Author:
Matthew Praeger
Author:
R.W. Eason
Author:
Benjamin Mills
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