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Lensless imaging of pollen grains at three-wavelength using deep learning

Lensless imaging of pollen grains at three-wavelength using deep learning
Lensless imaging of pollen grains at three-wavelength 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.
2515-7620
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
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
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
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-wavelength using deep learning. Environmental Research Communications. (doi:10.1088/2515-7620/aba6d1).

Record type: Article

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
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More information

Accepted/In Press date: 16 July 2020
e-pub ahead of print date: 16 July 2020

Identifiers

Local EPrints ID: 442540
URI: http://eprints.soton.ac.uk/id/eprint/442540
ISSN: 2515-7620
PURE UUID: e8a19c0a-8943-49d5-b1aa-fbdb6630e095
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for Matthew Loxham: ORCID iD orcid.org/0000-0001-6459-538X
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 17 Jul 2020 16:31
Last modified: 18 Feb 2021 17:23

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