Measuring multivariate subjective image quality for still and video cameras and image processing system components

2008 ◽  
Author(s):  
Göte Nyman ◽  
Tuomas Leisti ◽  
Paul Lindroos ◽  
Jenni Radun ◽  
Sini Suomi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Processing System ◽  
Subjective Image Quality ◽  
Image Processing System ◽  
Video Cameras ◽  
System Components

Development of High-Definition Image Processing System for a FE-SEM

1996 ◽  
Vol 54 ◽  
pp. 458-459
Author(s):  
M. Yamada ◽  
T. Yoshihara ◽  
H. Arima ◽  
Y. Nimura ◽  
T. Kobayashi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Real Time ◽  
Processing System ◽  
Digital Processing ◽  
High Definition ◽  
Sem Images ◽  
Pixel Resolution ◽  
Generator System ◽  
Image Processing System

This paper presents the development of a new, fully digital image processing system for a field emission SEM (FE-SEM) which can eliminate fluctuations in the emission current. The system can display, in real time, images with 1280 × 1024 pixels on a viewing CRT, allowing direct observation of high definition SEM images on the FE-SEM.In the past, even though image was acquired and recorded (photographed) with the resolution higher than 1,000 x 1,000 pixels, it was generally observed on the viewing CRT at the resolution of only about 500 x 500 pixels for real-time observation. This created a difference in image quality between the two CRT's. Using a CRT of the same pixel resolution for observation as on the recording CRT, the difference in image quality can be minimised, provided that the characteristics (speed and resolution) of the image processing and scan generator system are improved and optimised for digital processing.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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