Mirofiber based far-field optical super-resolution system

2012 ◽  
Author(s):  
Xiang Hao ◽  
Xu Liu ◽  
Cuifang Kuang ◽  
Limin Tong
Keyword(s):  
Super Resolution ◽  
Far Field ◽  
Resolution System

scholarly journals MINSTED fluorescence localization and nanoscopy

2021 ◽  
Author(s):  
Michael Weber ◽  
Marcel Leutenegger ◽  
Stefan Stoldt ◽  
Stefan Jakobs ◽  
Tiberiu S. Mihaila ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Stimulated Emission ◽  
Far Field ◽  
Inner Membrane ◽  
Mitochondrial Inner Membrane ◽  
Molecular Scale ◽  
Localization Precision ◽  
Super Resolution Microscopy

AbstractWe introduce MINSTED, a fluorophore localization and super-resolution microscopy concept based on stimulated emission depletion (STED) that provides spatial precision and resolution down to the molecular scale. In MINSTED, the intensity minimum of the STED doughnut, and hence the point of minimal STED, serves as a movable reference coordinate for fluorophore localization. As the STED rate, the background and the required number of fluorescence detections are low compared with most other STED microscopy and localization methods, MINSTED entails substantially less fluorophore bleaching. In our implementation, 200–1,000 detections per fluorophore provide a localization precision of 1–3 nm in standard deviation, which in conjunction with independent single fluorophore switching translates to a ~100-fold improvement in far-field microscopy resolution over the diffraction limit. The performance of MINSTED nanoscopy is demonstrated by imaging the distribution of Mic60 proteins in the mitochondrial inner membrane of human cells.

scholarly journals Optimising superoscillatory spots for far-field super-resolution imaging

2018 ◽  
Vol 26 (7) ◽  
pp. 8095 ◽  
Author(s):  
Katrine S. Rogers ◽  
Konstantinos N. Bourdakos ◽  
Guang Hui Yuan ◽  
Sumeet Mahajan ◽  
Edward T. F. Rogers
Keyword(s):  
Super Resolution ◽  
Far Field ◽  
Resolution Imaging

scholarly journals Far-field super-resolution ghost imaging with a deep neural network constraint

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Fei Wang ◽  
Chenglong Wang ◽  
Mingliang Chen ◽  
Wenlin Gong ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Far Field ◽  
Ghost Imaging ◽  
Practical Applications ◽  
Sampling Ratio ◽  
Network Output ◽  
Single Pixel

AbstractGhost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.

A Real-Time Coprime Line Scan Super-Resolution System for Ultra-Fast Microscopy

2019 ◽  
Vol 13 (4) ◽  
pp. 781-792
Author(s):  
Runbin Shi ◽  
Justin S. J. Wong ◽  
Edmund Y. Lam ◽  
Kevin K. Tsia ◽  
Hayden K.-H. So
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Line Scan ◽  
Resolution System
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