Image Processing Algorithms of Hartmann Aberration Automatic Measurement System Based on Tensor Product Network

The research status and limitations of the evaluation on the sensor performance are analyzed in detail, and the development direction of the evaluation on the performance of the Hartmann Aberration Automatic Measurement System is pointed out. Based on the clarification of the Hartmann Aberration Aut...

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Bibliographic Details
Published inInternational journal of wireless information networks Vol. 26; no. 3; pp. 158 - 164
Main Author Chen, Yi-bing
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2019
Springer Nature B.V
Subjects
Aberration
Adaptive systems
Algorithms
Edge detection
Electrical Engineering
Engineering
Image detection
Image processing
Image segmentation
Infrared radiation
Production planning
Tensors
Effectiveness evaluation
Words-Hartmann Aberration Automatic Measurement System
Image processing algorithms
Low grey edge information
Online AccessGet full text
ISSN1068-9605
1572-8129
DOI10.1007/s10776-019-00435-w

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Summary:The research status and limitations of the evaluation on the sensor performance are analyzed in detail, and the development direction of the evaluation on the performance of the Hartmann Aberration Automatic Measurement System is pointed out. Based on the clarification of the Hartmann Aberration Automatic Measurement System with the operational effectiveness and the difference in the operational effectiveness, the connotation of the Hartmann Aberration Automatic Measurement System with the operational effectiveness is expounded. A kind of Image Processing Algorithms based on the consideration of the Hartmann Aberration Automatic Measurement System is put forward. The membership function is changed to simplify the calculation and reduce processing time. Secondly, the adaptive method based on the Hartmann Aberration Automatic Measurement System is applied to the process of selecting the segmentation threshold value, and the threshold values of different images are obtained to make the segmentation more accurate. The experimental results show that compared with the traditional Pal-King algorithm, the algorithm put forward in this paper can preserve the low grey edge information of the image and reduce the computation time. Therefore, it can be applied to the field of image fuzzy edge detection.
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ISSN:1068-9605
1572-8129
DOI:10.1007/s10776-019-00435-w