scholarly journals Divide and Conquer: Real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy

2019 ◽  
Author(s):  
Luchang Li ◽  
Bo Xin ◽  
Weibing Kuang ◽  
Zhiwei Zhou ◽  
Zhen-Li Huang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Super Resolution ◽  
Large Cell ◽  
Field Of View ◽  
Divide And Conquer ◽  
Image Size ◽  
Localization Microscopy ◽  
Localization Algorithms ◽  
Emitter Localization

AbstractMulti-emitter localization has great potential for maximizing the imaging speed of super-resolution localization microscopy. However, the slow image analysis speed of reported multi-emitter localization algorithms limits their usage in mostly off-line image processing with small image size. Here we adopt the well-known divide and conquer strategy in computer science and present a fitting-based method called QC-STORM for fast multi-emitter localization. Using simulated and experimental data, we verify that QC-STORM is capable of providing real-time full image processing on raw images with 100 µm × 100 µm field of view and 10 ms exposure time, with comparable spatial resolution as the popular fitting-based ThunderSTORM and the up-to-date non-iterative WindSTORM. This study pushes the development and practical use of super-resolution localization microscopy in high-throughput or high-content imaging of cell-to-cell differences or discovering rare events in a large cell population.

scholarly journals Divide and conquer: real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy

2019 ◽  
Vol 27 (15) ◽  
pp. 21029 ◽  
Author(s):  
Luchang Li ◽  
Bo Xin ◽  
Weibing Kuang ◽  
Zhiwei Zhou ◽  
Zhen-Li Huang
Keyword(s):  
Maximum Likelihood ◽  
Real Time ◽  
Super Resolution ◽  
Divide And Conquer ◽  
Localization Microscopy

scholarly journals An Application based Efficient Thread Level Parallelism Scheme on Heterogeneous Multicore Embedded System for Real Time Image Processing.

2020 ◽  
Vol 21 (1) ◽  
pp. 47-56
Author(s):  
K Indragandhi ◽  
Jawahar P K
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Embedded System ◽  
Real Time ◽  
Execution Time ◽  
Image Size ◽  
Multicore Processor ◽  
Detection Scheme ◽  
Thread Level Parallelism ◽  
Level Parallelism

The recent advent of the embedded devices is equipped with multicore processor as it significantly improves the system performance. In order to utilize all the core in multicore processor in an efficient manner, application programs need to be parallelized. An efficient thread level parallelism (ETLP) scheme is proposed in this paper and uses computationally intensive edge detection algorithm for evaluation. Edge detection is the important process in various real time applications namely vehicle detection in traffic control, medical image processing etc. The main objective of ETLP scheme is to reduce the execution time and increase the CPU core utilization. The performance of ETLP scheme is evaluated with basic edge detection scheme (BEDS) for different image size. The experimental results reveal that the proposed ETLP scheme achieves efficiency of 49% and 72% for the image size 300 x 256 and 1024 x 1024 respectively. Furthermore an ETLP scheme reducing 66% execution time for image size 1024 x 1024 when compared with BEDS.

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 High-power homogeneous illumination for super-resolution localization microscopy with large field-of-view

2017 ◽  
Vol 25 (12) ◽  
pp. 13382 ◽  
Author(s):  
Zeyu Zhao ◽  
Bo Xin ◽  
Luchang Li ◽  
Zhen-Li Huang
Keyword(s):  
High Power ◽  
Super Resolution ◽  
Field Of View ◽  
Large Field ◽  
Localization Microscopy

scholarly journals Real-time adaptive drift correction for super-resolution localization microscopy

2015 ◽  
Vol 23 (18) ◽  
pp. 23887 ◽  
Author(s):  
Ginni Grover ◽  
Wyatt Mohrman ◽  
Rafael Piestun
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Drift Correction ◽  
Localization Microscopy
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