scholarly journals Structure based analysis of protein cluster size for super-resolution microscopy in the nervous system

2019 ◽  
Author(s):  
Chia-En Wong ◽  
Cheng-Che Lee ◽  
Kuen-Jer Tsai
Keyword(s):  
Super Resolution ◽  
Superior Performance ◽  
Structural Function ◽  
Wide Field ◽  
Localization Algorithm ◽  
Novel Structure ◽  
Localization Algorithms ◽  
Optical Reconstruction ◽  
Microscopy Data ◽  
Maximum Parameters

AbstractTo overcome the diffraction limit and resolve target structures in greater detail, far-field super-resolution techniques such as stochastic optical reconstruction microscopy (STORM) have been developed, and different STORM algorithms have been developed to deal with the various problems that arise. In particular, the effect of local structure is an important issue. For objects with closely correlated distributions, simple Gaussian-based localization algorithms often used in STORM imaging misinterpret overlapping point spread functions (PSFs) as one and this limits the ability of super-resolution imaging to resolve nanoscale local structures and leading to inaccurate length measurements. In the present study, we proposed a novel, structure-based, super-resolution image analysis method: structure-based analysis (SBA), which combines a structural function and a super-resolution localization algorithm. Using SBA, we estimated the size of fluorescent beads, inclusion proteins, and subtle synaptic structures in both wide-field and STORM images. The results showed that SBA has comparable and often superior performance to commonly used full-width-at-half-maximum parameters. We also demonstrated that SBA provides size estimations that corroborate previously published electron microscopy data.

scholarly journals Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network

Photonics ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 321
Author(s):  
Bowen Wang ◽  
Yan Zou ◽  
Linfei Zhang ◽  
Yan Hu ◽  
Hao Yan ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Light Level ◽  
Superior Performance ◽  
Wide Field ◽  
Low Light ◽  
Color Mapping ◽  
Multi Scale ◽  
Imaging Sensors

Wide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current imaging sensors to obtain high spatial resolution is limited, especially under low-light-level (LLL) imaging conditions. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize pixel-super-resolved LLL imaging. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, the calculation of the high-frequency components can receive greater attention. Compared with other networks, the peak signal-to-noise ratio of the reconstructed image was increased by 1.67 dB. Extensions of the MSFE network are investigated for scene-based color mapping of the gray image. Most of the color information could be recovered, and the similarity with the real image reached 0.728. The qualitative and quantitative experimental results show that the proposed method achieved superior performance in image fidelity and detail enhancement over the state-of-the-art.

scholarly journals Analyzing Single Molecule Localization Microscopy Data Using Cubic Splines

2016 ◽  
Author(s):  
Hazen P. Babcock ◽  
Xiaowei Zhuang
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Computation Time ◽  
Cubic Splines ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Analytical Functions ◽  
Open Source Software Package ◽  
Microscopy Data ◽  
Single Molecule Localization Microscopy

AbstractThe resolution of super-resolution microscopy based on single molecule localization is in part determined by the accuracy of the localization algorithm. In most published approaches to date this localization is done by fitting an analytical function that approximates the point spread function (PSF) of the microscope. However, particularly for localization in 3D, analytical functions such as a Gaussian, which are computationally inexpensive, may not accurately capture the PSF shape leading to reduced fitting accuracy. On the other hand, analytical functions that can accurately capture the PSF shape, such as those based on pupil functions, can be computationally expensive. Here we investigate the use of cubic splines as an alternative fitting approach. We demonstrate that cubic splines can capture the shape of any PSF with high accuracy and that they can be used for fitting the PSF with only a 2-3x increase in computation time as compared to Gaussian fitting. We provide an open-source software package that measures the PSF of any microscope and uses the measured PSF to perform 3D single molecule localization microscopy analysis with reasonable accuracy and speed.

scholarly journals Comparing Super-Resolution Microscopy Techniques to Analyze Chromosomes

2021 ◽  
Vol 22 (4) ◽  
pp. 1903
Author(s):  
Ivona Kubalová ◽  
Alžběta Němečková ◽  
Klaus Weisshart ◽  
Eva Hřibová ◽  
Veit Schubert
Keyword(s):  
Single Molecule ◽  
Imaging Techniques ◽  
Chromatin Condensation ◽  
Super Resolution ◽  
Stimulated Emission ◽  
Wide Field ◽  
Structured Illumination Microscopy ◽  
Metaphase Chromosomes ◽  
Localization Microscopy ◽  
Super Resolution Microscopy

The importance of fluorescence light microscopy for understanding cellular and sub-cellular structures and functions is undeniable. However, the resolution is limited by light diffraction (~200–250 nm laterally, ~500–700 nm axially). Meanwhile, super-resolution microscopy, such as structured illumination microscopy (SIM), is being applied more and more to overcome this restriction. Instead, super-resolution by stimulated emission depletion (STED) microscopy achieving a resolution of ~50 nm laterally and ~130 nm axially has not yet frequently been applied in plant cell research due to the required specific sample preparation and stable dye staining. Single-molecule localization microscopy (SMLM) including photoactivated localization microscopy (PALM) has not yet been widely used, although this nanoscopic technique allows even the detection of single molecules. In this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM. The chromosomes were labeled by DAPI (4′,6-diamidino-2-phenylindol), a DNA-specific dye, and with antibodies against topoisomerase IIα (Topo II), a protein important for correct chromatin condensation. Compared to the diffraction-limited methods, the combination of the three different super-resolution imaging techniques delivered tremendous additional insights into the plant chromosome architecture through the achieved increased resolution.

Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

scholarly journals Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2400
Author(s):  
Ziyong Zhang ◽  
Xiaoling Xu ◽  
Jinqiang Cui ◽  
Wei Meng
Keyword(s):  
Quadratic Programming ◽  
Wide Band ◽  
Area Coverage ◽  
Localization Algorithm ◽  
Placement Problem ◽  
Position Information ◽  
Relative Localization ◽  
Coverage Control ◽  
Localization Algorithms ◽  
Multi Uav

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

scholarly journals Camera-based localization microscopy optimized with calibrated structured illumination

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Martin Schmidt ◽  
Adam C. Hundahl ◽  
Henrik Flyvbjerg ◽  
Rodolphe Marie ◽  
Kim I. Mortensen
Keyword(s):  
Data Analysis ◽  
Super Resolution ◽  
Wide Field ◽  
Structured Illumination ◽  
Uniform Illumination ◽  
Localization Microscopy ◽  
Localization And Tracking ◽  
Field Imaging ◽  
Wide Field Imaging ◽  
Localization Information

AbstractUntil very recently, super-resolution localization and tracking of fluorescent particles used camera-based wide-field imaging with uniform illumination. Then it was demonstrated that structured illuminations encode additional localization information in images. The first demonstration of this uses scanning and hence suffers from limited throughput. This limitation was mitigated by fusing camera-based localization with wide-field structured illumination. Current implementations, however, use effectively only half the localization information that they encode in images. Here we demonstrate how all of this information may be exploited by careful calibration of the structured illumination. Our approach achieves maximal resolution for given structured illumination, has a simple data analysis, and applies to any structured illumination in principle. We demonstrate this with an only slightly modified wide-field microscope. Our protocol should boost the emerging field of high-precision localization with structured illumination.

Wide field coded aperture super resolution imaging

2021 ◽  
Author(s):  
Bowen Wang ◽  
Yan Zou ◽  
Chao Zuo
Keyword(s):  
Super Resolution ◽  
Coded Aperture ◽  
Wide Field ◽  
Resolution Imaging

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

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