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Image reconstruction of pollen grains from their scattering pattern using deep learning

Image reconstruction of pollen grains from their scattering pattern using deep learning
Image reconstruction of pollen grains from their scattering pattern using deep learning
Airborne pollution particulates are associated with approximately 40,000 deaths per year in the UK. Specific types of particulates are thought to be more toxic to people with certain conditions, and hence precise identification of the numbers and types that are airborne at any particular place or time is a key strategy in reducing adverse health effects within the population. A specific example, and one that is used as the motivation for this work, is the effect of pollen grains on individuals who may have allergies to none, some, or all types of pollen.

In order to categorise pollen grains that are airborne across populated areas, a rapid and precise sensing system is required that includes all imaging and computational components, and that is also small, low maintenance and low-cost. As standard optical microscopy approaches for imaging and characterising micron-sized structures require costly setups, a lensless imaging approach is more suitable. However, lensless approaches such as phase retrieval are generally computationally intensive, and hence not appropriate for a low-cost sensor. Accordingly, here we demonstrate the capability of deep learning for rapid lensless image reconstruction from the scattering patterns of a variety of pollen grains. Specifically, we extend work on using deep learning to identify the type of pollen grains from scatterin patterns, by using a conditional generative adversarial network (cGAN) to generate 50x magnification images of pollen grains directly from their scattering patterns.
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
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Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert W.
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Mills, Benjamin
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Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, Praeger, Matthew, Loxham, Matthew, Eason, Robert W. and Mills, Benjamin (2020) Image reconstruction of pollen grains from their scattering pattern using deep learning. Institute of Physics - Photon 2020: IoP Webinars for the Physics Community (virtual conference), United Kingdom. 01 - 04 Sep 2020. 1 pp .

Record type: Conference or Workshop Item (Other)

Abstract

Airborne pollution particulates are associated with approximately 40,000 deaths per year in the UK. Specific types of particulates are thought to be more toxic to people with certain conditions, and hence precise identification of the numbers and types that are airborne at any particular place or time is a key strategy in reducing adverse health effects within the population. A specific example, and one that is used as the motivation for this work, is the effect of pollen grains on individuals who may have allergies to none, some, or all types of pollen.

In order to categorise pollen grains that are airborne across populated areas, a rapid and precise sensing system is required that includes all imaging and computational components, and that is also small, low maintenance and low-cost. As standard optical microscopy approaches for imaging and characterising micron-sized structures require costly setups, a lensless imaging approach is more suitable. However, lensless approaches such as phase retrieval are generally computationally intensive, and hence not appropriate for a low-cost sensor. Accordingly, here we demonstrate the capability of deep learning for rapid lensless image reconstruction from the scattering patterns of a variety of pollen grains. Specifically, we extend work on using deep learning to identify the type of pollen grains from scatterin patterns, by using a conditional generative adversarial network (cGAN) to generate 50x magnification images of pollen grains directly from their scattering patterns.

Text
Photon2020_8 - Accepted Manuscript
Available under License Creative Commons Attribution.
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More information

Published date: 4 September 2020
Venue - Dates: Institute of Physics - Photon 2020: IoP Webinars for the Physics Community (virtual conference), United Kingdom, 2020-09-01 - 2020-09-04

Identifiers

Local EPrints ID: 443660
URI: http://eprints.soton.ac.uk/id/eprint/443660
PURE UUID: 95480258-e4d8-4e1b-8c49-d082430e4838
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 Robert 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: 07 Sep 2020 16:31
Last modified: 18 Feb 2021 17:23

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