3-D super-resolution localization microscopy using deep-learning method

2019 ◽  
Author(s):  
Mengyang Lu ◽  
Tianyang Zhou ◽  
Xin Liu
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Learning Method ◽  
Localization Microscopy

A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

2020 ◽  
Author(s):  
Anish Mukherjee
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Point Sources ◽  
Learning Approaches ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Trade Offs ◽  
Emitter Localization ◽  
Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

scholarly journals Applications of a deep learning method for anti-aliasing and super-resolution in MRI

2019 ◽  
Vol 64 ◽  
pp. 132-141 ◽  
Author(s):  
Can Zhao ◽  
Muhan Shao ◽  
Aaron Carass ◽  
Hao Li ◽  
Blake E. Dewey ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Learning Method

scholarly journals Design Principles for Autonomous Illumination Control in Localization Microscopy

2018 ◽  
Author(s):  
Marcel Štefko ◽  
Baptiste Ottino ◽  
Kyle M. Douglass ◽  
Suliana Manley
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Fluorescence Microscopy ◽  
A Priori ◽  
Super Resolution ◽  
Density Estimator ◽  
Localization Microscopy ◽  
Extended Range ◽  
Self Tuning ◽  
Fine Tune

Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning molecule density estimator that is accurate over an extended range. This system obviates the need to fine-tune parameters and demonstrates the design of modular illumination control for localization microscopy.

scholarly journals Deep learning massively accelerates super-resolution localization microscopy

2018 ◽  
Vol 36 (5) ◽  
pp. 460-468 ◽  
Author(s):  
Wei Ouyang ◽  
Andrey Aristov ◽  
Mickaël Lelek ◽  
Xian Hao ◽  
Christophe Zimmer
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Localization Microscopy

scholarly journals Super-resolution Ultrasound Localization Microscopy through Deep Learning

2020 ◽  
pp. 1-1
Author(s):  
Ruud JG van Sloun ◽  
Oren Solomon ◽  
Matthew Bruce ◽  
Zin Z Khaing ◽  
Hessel Wijkstra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Localization Microscopy ◽  
Ultrasound Localization

scholarly journals A Wavelet Based Deep Learning Method for Underwater Image Super Resolution Reconstruction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 117759-117769
Author(s):  
Yuzhang Chen ◽  
Kangli Niu ◽  
Zhangfan Zeng ◽  
Yongcai Pan
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Learning Method ◽  
Underwater Image ◽  
Image Super Resolution
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