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

摘要

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.