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Unlabeled far-field Deeply Subwavelength Topological Microscopy (DSTM)

Unlabeled far-field Deeply Subwavelength Topological Microscopy (DSTM)
Unlabeled far-field Deeply Subwavelength Topological Microscopy (DSTM)
A nonintrusive far-field optical microscopy resolving structures at the nanometer scale would revolutionize biomedicine and nanotechnology but is not yet available. Here, a new type of microscopy is introduced, which reveals the fine structure of an object through its far-field scattering pattern under illumination with light containing deeply subwavelength singularity features. The object is reconstructed by a neural network trained on a large number of scattering events. In numerical experiments on imaging of a dimer, resolving powers better than λ/200, i.e., two orders of magnitude beyond the conventional “diffraction limit” of λ/2, are demonstrated. It is shown that imaging is tolerant to noise and is achievable with low dynamic range light intensity detectors. Proof-of-principle experimental confirmation of DSTM is provided with a training set of small size, yet sufficient to achieve resolution five-fold better than the diffraction limit. In principle, deep learning reconstruction can be extended to objects of random shape and shall be particularly efficient in microscopy of a priori known shapes, such as those found in routine tasks of machine vision, smart manufacturing, and particle counting for life sciences applications.
machine learning, microscopy, superoscillations, superresolution, unlabeled
2198-3844
Pu, Tanchao
89eb5a37-31bf-469a-ae29-c871d5d25c65
Ou, Jun-Yu
3fb703e3-b222-46d2-b4ee-75f296d9d64d
Savinov, Vassili
147c7954-4636-4438-a305-cd78539f7c0a
Yuan, Guanghui
d7af6f06-7da9-41ef-b7f9-cfe09e55fcaa
Papasimakis, Nikitas
f416bfa9-544c-4a3e-8a2d-bc1c11133a51
Zheludev, Nikolai
32fb6af7-97e4-4d11-bca6-805745e40cc6
Pu, Tanchao
89eb5a37-31bf-469a-ae29-c871d5d25c65
Ou, Jun-Yu
3fb703e3-b222-46d2-b4ee-75f296d9d64d
Savinov, Vassili
147c7954-4636-4438-a305-cd78539f7c0a
Yuan, Guanghui
d7af6f06-7da9-41ef-b7f9-cfe09e55fcaa
Papasimakis, Nikitas
f416bfa9-544c-4a3e-8a2d-bc1c11133a51
Zheludev, Nikolai
32fb6af7-97e4-4d11-bca6-805745e40cc6

Pu, Tanchao, Ou, Jun-Yu, Savinov, Vassili, Yuan, Guanghui, Papasimakis, Nikitas and Zheludev, Nikolai (2020) Unlabeled far-field Deeply Subwavelength Topological Microscopy (DSTM). Advanced Science. (doi:10.1002/advs.202002886).

Record type: Article

Abstract

A nonintrusive far-field optical microscopy resolving structures at the nanometer scale would revolutionize biomedicine and nanotechnology but is not yet available. Here, a new type of microscopy is introduced, which reveals the fine structure of an object through its far-field scattering pattern under illumination with light containing deeply subwavelength singularity features. The object is reconstructed by a neural network trained on a large number of scattering events. In numerical experiments on imaging of a dimer, resolving powers better than λ/200, i.e., two orders of magnitude beyond the conventional “diffraction limit” of λ/2, are demonstrated. It is shown that imaging is tolerant to noise and is achievable with low dynamic range light intensity detectors. Proof-of-principle experimental confirmation of DSTM is provided with a training set of small size, yet sufficient to achieve resolution five-fold better than the diffraction limit. In principle, deep learning reconstruction can be extended to objects of random shape and shall be particularly efficient in microscopy of a priori known shapes, such as those found in routine tasks of machine vision, smart manufacturing, and particle counting for life sciences applications.

Text
DSTM Revised manuscript - Accepted Manuscript
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 29 September 2020
e-pub ahead of print date: 17 November 2020
Keywords: machine learning, microscopy, superoscillations, superresolution, unlabeled

Identifiers

Local EPrints ID: 444503
URI: http://eprints.soton.ac.uk/id/eprint/444503
ISSN: 2198-3844
PURE UUID: d8499baa-0298-4819-a057-da78100fa25f
ORCID for Tanchao Pu: ORCID iD orcid.org/0000-0002-1782-5653
ORCID for Jun-Yu Ou: ORCID iD orcid.org/0000-0001-8028-6130
ORCID for Vassili Savinov: ORCID iD orcid.org/0000-0001-7203-7222
ORCID for Nikitas Papasimakis: ORCID iD orcid.org/0000-0002-6347-6466
ORCID for Nikolai Zheludev: ORCID iD orcid.org/0000-0002-1013-6636

Catalogue record

Date deposited: 22 Oct 2020 16:30
Last modified: 07 Oct 2021 02:03

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