Abstract

Detecting the interference fringes of the optical synthetic aperture is the core in preventing misalignments of the sub-mirrors in piston, tip, and tilt. These fringes are characterized as follows: (1) the edge information of sub-mirrors is accompanied by complex shapes and large gaps; and (2) the traditional edge detection algorithms have different optimal thresholds under different interference fringes, and they may lose boundary information. To address these problems, a novel method for detecting the edge of synthetic aperture fringe images is proposed. Because conditional generative adversarial networks avoid the difficulty of designing the loss function for specific tasks, they are suitable for our project. We trained over 8000 images based on real images and simulated images. Experiments prove that the proposed method can reduce the false detection rate to 0.2, compared with 0.56 by Canny algorithm. This method can also directly detect the fringe edge of the optical synthetic aperture systems, which are accompanied by varied shapes and a growing number of sub-mirrors. When the input images lose boundary information, the traditional algorithm does not restore the boundary, but the proposed method makes a decision globally, and thus it guesses and then fills the boundary. The maximum error of the generated boundary and the actual boundary is two pixels.

© 2019 Optical Society of America

Visualizing the prediction of laser cleaning: a dynamic preview method with a multi-scale conditional generative adversarial network

YingHui Zhang, YiJia Zhao, Bo Sun, and Jun He
Appl. Opt. 58(31) 8344-8353 (2019)

Training generative adversarial networks for optical property mapping using synthetic image data

A. Osman, J. Crowley, and G. S. D Gordon
Biomed. Opt. Express 13(10) 5171-5186 (2022)

Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network

Rui Zheng, Lei Liu, Shulin Zhang, Chun Zheng, Filiz Bunyak, Ronald Xu, Bin Li, and Mingzhai Sun
Biomed. Opt. Express 9(10) 4863-4878 (2018)

CTIS-GAN: computed tomography imaging spectrometry based on a generative adversarial network

Luoxiang Wu and Weiwei Cai
Appl. Opt. 62(10) 2422-2433 (2023)

Speckle denoising based on deep learning via a conditional generative adversarial network in digital holographic interferometry

Qiang Fang, Haiting Xia, Qinghe Song, Meijuan Zhang, Rongxin Guo, Silvio Montresor, and Pascal Picart
Opt. Express 30(12) 20666-20683 (2022)