Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Machine learning assisted quantum super-resolution microscopy

Not Accessible

Your library or personal account may give you access

Abstract

A machine learning assisted framework significantly speeds up image acquisition in super-resolution microscopy based on photon antibunching. The technique is compatible with a CW excitation regime and applicable to a wide range of quantum emitters.

© 2021 The Author(s)

PDF Article  |   Presentation Video
More Like This
Machine Learning Assisted Quantum Photonics

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
QM6B.3 Quantum 2.0 (QUANTUM) 2020

Merging Machine Learning with Quantum Photonics: Rapid classification of quantum sources

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
FM4C.4 CLEO: QELS_Fundamental Science (CLEO:FS) 2020

Thickness identification of 2D materials by machine learning assisted optical microscopy

Daniele Gaetano Sirico, Giovanni Acampora, Pasqualino Maddalena, and Felice Gesuele
JTh3A.8 CLEO: Applications and Technology (CLEO:A&T) 2021

Presentation Video

Presentation video access is available to:

  1. Optica Publishing Group subscribers
  2. Technical meeting attendees
  3. Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads


More Like This
Machine Learning Assisted Quantum Photonics

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
QM6B.3 Quantum 2.0 (QUANTUM) 2020

Merging Machine Learning with Quantum Photonics: Rapid classification of quantum sources

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
FM4C.4 CLEO: QELS_Fundamental Science (CLEO:FS) 2020

Thickness identification of 2D materials by machine learning assisted optical microscopy

Daniele Gaetano Sirico, Giovanni Acampora, Pasqualino Maddalena, and Felice Gesuele
JTh3A.8 CLEO: Applications and Technology (CLEO:A&T) 2021

Machine Learning Assisted Management of Photonic Switching Systems

Ihtesham Khan, M Umar Masood, Lorenzo Tunesi, Paolo Bardella, Enrico Ghillino, Andrea Carena, and Vittorio Curri
JTu3A.32 CLEO: Applications and Technology (CLEO:A&T) 2021

Single-Molecule Super-Resolution Microscopy with Light Field and Deep Learning

Keyi Han, Xuanwen Hua, Xiaopeng Wang, and Shu Jia
FM1E.6 Frontiers in Optics (FiO) 2023

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.