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A neural lens for super-resolution biological imaging

A neural lens for super-resolution biological imaging
A neural lens for super-resolution biological imaging
Visualizing structures smaller than the eye can see has been a driving force in scientific research since the invention of the optical microscope. Here, we use a network of neural networks to create a neural lens that has the ability to transform 20× optical microscope images into a resolution comparable to a 1500× scanning electron microscope image. In addition to magnification, the neural lens simultaneously identifies the types of objects present, and hence can label, colour-enhance and remove specific types of objects in the magnified image. The neural lens was used for the imaging of Iva xanthiifolia and Galanthus pollen grains, showing the potential for low cost, non-destructive, high-resolution microscopy with automatic image processing.
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Loxham, Matthew
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Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, MacKay, Benita, Scout, Xie, Yunhui, Heath, Daniel J, Loxham, Matthew, Eason, Robert and Mills, Benjamin (2019) A neural lens for super-resolution biological imaging. Journal of Physics Communications. (doi:10.1088/2399-6528/ab267d).

Record type: Article

Abstract

Visualizing structures smaller than the eye can see has been a driving force in scientific research since the invention of the optical microscope. Here, we use a network of neural networks to create a neural lens that has the ability to transform 20× optical microscope images into a resolution comparable to a 1500× scanning electron microscope image. In addition to magnification, the neural lens simultaneously identifies the types of objects present, and hence can label, colour-enhance and remove specific types of objects in the magnified image. The neural lens was used for the imaging of Iva xanthiifolia and Galanthus pollen grains, showing the potential for low cost, non-destructive, high-resolution microscopy with automatic image processing.

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More information

Accepted/In Press date: 31 May 2019
e-pub ahead of print date: 17 June 2019

Identifiers

Local EPrints ID: 431978
URI: https://eprints.soton.ac.uk/id/eprint/431978
PURE UUID: 2e3eae6f-1e2a-47fd-87c2-a2272232fe8e
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benita, Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Matthew Loxham: ORCID iD orcid.org/0000-0001-6459-538X
ORCID for Robert 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: 25 Jun 2019 16:30
Last modified: 26 Jun 2019 00:39

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