scholarly journals Improving Image Quality and Reducing Drift Problems via Automated Data Acquisition and Averaging in a Cs-Corrected TEM

2008 ◽  
Vol 16 (6) ◽  
pp. 36-39 ◽  
Author(s):  
E. Voelkl ◽  
B. Jiang ◽  
Z.R. Dai ◽  
J.P Bradley
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Data Acquisition ◽  
Information Content ◽  
Exposure Time ◽  
Image Acquisition ◽  
Ccd Camera ◽  
Automated Data Acquisition ◽  
Processing Steps

Image acquisition with a CCD camera is a single-press-button activity: after selecting exposure time and adjusting illumination, a button is pressed and the acquired image is perceived as the final, unmodified proof of what was seen in the microscope. Thus it is generally assumed that the image processing steps of e.g., “darkcurrent correction” and “gain normalization” do not alter the information content of the image, but rather eliminate unwanted artifacts.

Designation of Image Acquisition Module Based on the C8051F340

2012 ◽  
Vol 588-589 ◽  
pp. 769-772
Author(s):  
Xin Bin Zhang ◽  
Shi Zhong Li
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Data Acquisition ◽  
Simple Structure ◽  
Image Acquisition ◽  
Image Data ◽  
Image Sensor ◽  
Software Implementation ◽  
Wireless Transmission ◽  
Data Acquisition Module

The image data acquisition module mainly completes the collection of image data and transmits the collected data to the wireless transmission module. This paper mainly discusses the hardware components of the image acquisition module and software implementation. The collected image quality influences image processing, therefore the choice of image sensor is an important part of this system. This is a OV7670 image sensor. The master controller uses C8051F340 MCU. C8051F MC’s frequency has been greatly improved compared with ordinary 51 MCU, and has the advantages of simple structure, interface expansion capability, low prices and better performance.

Study on Image Processing of Wheel Valve Hole Based on Machine Vision

2011 ◽  
Vol 63-64 ◽  
pp. 541-546 ◽  
Author(s):  
Chang Chun Li ◽  
Shi Feng Wang ◽  
Jing Yu ◽  
Hua Guan Liu
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Machine Vision ◽  
Edge Detection ◽  
Basic Principle ◽  
Image Acquisition ◽  
Wheel Speed ◽  
Hole Edge

This paper discusses the basic principle for automatic searching the wheel valve hole based on machine vision. Image acquisition and image processing have been done, and we analyzed the factors that impact the image quality of wheel valve hole. This paper argues that many parameters such as the wheel speed, painting color, the distance between the camera and the valve hole, edge detection operator, and they will affect the quality of the image acquisition and image processing of valve hole.

scholarly journals Improving Image Quality and Reducing Drift Problems via Automated Data Acquisition and Averaging in Cs-corrected TEM

2008 ◽  
Vol 14 (S2) ◽  
pp. 844-845 ◽  
Author(s):  
E Voelkl ◽  
B Jiang ◽  
ZR Dai ◽  
JP Bradley
Keyword(s):  
New Mexico ◽  
Image Quality ◽  
Data Acquisition ◽  
Automated Data Acquisition

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Live Electron Holography: Window to the Phase World

2000 ◽  
Vol 6 (3) ◽  
pp. 211-217 ◽  
Author(s):  
E. Völkl
Keyword(s):  
Image Processing ◽  
Image Acquisition ◽  
Digital System ◽  
Electron Holography ◽  
Reconstruction Procedure ◽  
Current Time ◽  
First Report ◽  
Technical Requirements ◽  
Single Frame ◽  
Processing Steps

Abstract While the idea of electron holography has been around for over 50 years, the technology necessary to establish a window to the phase world is only now becoming available. This is the first report of an all digital system for displaying the image phase continuously and live, instead of looking at one frame at a time after initiation of the reconstruction procedure. The technical requirements, including the necessary image processing steps which differ from the standard reconstruction procedure for single frame holograms, will be discussed, and current time demands for image acquisition and the different elements of image processing will be laid out. The modified requirements for continuous, live processing will be discussed in detail, and images obtained in the live mode will be shown.

Live Electron Holography: Window to the Phase World

2000 ◽  
Vol 6 (3) ◽  
pp. 211-217 ◽  
Author(s):  
E. Völkl
Keyword(s):  
Image Processing ◽  
Image Acquisition ◽  
Digital System ◽  
Electron Holography ◽  
Reconstruction Procedure ◽  
Current Time ◽  
First Report ◽  
Technical Requirements ◽  
Single Frame ◽  
Processing Steps

AbstractWhile the idea of electron holography has been around for over 50 years, the technology necessary to establish a window to the phase world is only now becoming available. This is the first report of an all digital system for displaying the image phase continuously and live, instead of looking at one frame at a time after initiation of the reconstruction procedure. The technical requirements, including the necessary image processing steps which differ from the standard reconstruction procedure for single frame holograms, will be discussed, and current time demands for image acquisition and the different elements of image processing will be laid out. The modified requirements for continuous, live processing will be discussed in detail, and images obtained in the live mode will be shown.

Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

1977 ◽  
Vol 35 ◽  
pp. 214-217
Author(s):  
F. A. Heckman ◽  
E. Redman ◽  
J.E. Connolly
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Exposure Time ◽  
Image Contrast ◽  
Automated Image Analysis ◽  
Practical Application ◽  
Factors Affecting ◽  
High Image Quality ◽  
Qualitative Evidence ◽  
Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

Automated electron tomography: from data collection to image processing

1995 ◽  
Vol 53 ◽  
pp. 26-27
Author(s):  
Weiping Liu ◽  
Jennifer Fung ◽  
W.J. de Ruijter ◽  
Hans Chen ◽  
John W. Sedat ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Collection ◽  
Structural Information ◽  
Imaging Technique ◽  
Electron Tomography ◽  
Ccd Camera ◽  
Automated Data Collection ◽  
Full Control ◽  
Transmission Electron ◽  
Amorphous Structures

Electron tomography is a technique where many projections of an object are collected from the transmission electron microscope (TEM), and are then used to reconstruct the object in its entirety, allowing internal structure to be viewed. As vital as is the 3-D structural information and with no other 3-D imaging technique to compete in its resolution range, electron tomography of amorphous structures has been exercised only sporadically over the last ten years. Its general lack of popularity can be attributed to the tediousness of the entire process starting from the data collection, image processing for reconstruction, and extending to the 3-D image analysis. We have been investing effort to automate all aspects of electron tomography. Our systems of data collection and tomographic image processing will be briefly described.To date, we have developed a second generation automated data collection system based on an SGI workstation (Fig. 1) (The previous version used a micro VAX). The computer takes full control of the microscope operations with its graphical menu driven environment. This is made possible by the direct digital recording of images using the CCD camera.

A basic introduction to image processing using NIH-Image as a model

1994 ◽  
Vol 52 ◽  
pp. 392-393
Author(s):  
John Mansfield
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Acquisition ◽  
Digital Instrument ◽  
Novice User ◽  
Nih Image ◽  
The Past ◽  
Digital World ◽  
The World ◽  
Instrument Control

Advances in camera technology and digital instrument control have meant that in modern microscopy, the image that was, in the past, typically recorded on a piece of film is now recorded directly into a computer. The transfer of the analog image seen in the microscope to the digitized picture in the computer does not mean, however, that the problems associated with recording images, analyzing them, and preparing them for publication, have all miraculously been solved. The steps involved in the recording an image to film remain largely intact in the digital world. The image is recorded, prepared for measurement in some way, analyzed, and then prepared for presentation.Digital image acquisition schemes are largely the realm of the microscope manufacturers, however, there are also a multitude of “homemade” acquisition systems in microscope laboratories around the world. It is not the mission of this tutorial to deal with the various acquisition systems, but rather to introduce the novice user to rudimentary image processing and measurement.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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