Super-resolution Imaging of Telescopic Systems based on Optical-neural Network Joint Optimization  

作　　者：You-Hong Sun Tao Zhang Hao-Dong Shi Qiang Fu Jia-Nan Liu Kai-Kai Wang Chao Wang 

机构地区：[1]Research Institute of Chongqing,Changchun University of Science and Technology,Changchun 130022,China [2]National and Local Joint Engineering Research Center for Space Photoelectric Technology,Changchun University of Science and Technology,Changchun 130022,China [3]The Institute of Remote Sensing Satellites,China Academy of Space Technology(CAST),Beijing 100094,China

出　　处：《Research in Astronomy and Astrophysics》2024年第9期167-177,共11页天文和天体物理学研究（英文版）

基　　金：Funding is provided by the National Natural Science Foundation of China(NSFC,Grant Nos.62375027 and 62127813);Natural Science Foundation of Chongqing Municipality(CSTB2023NSCQ-MSX0504);Natural Science Foundation of Jilin Provincial(YDZJ202201ZYTS411);Jilin Provincial Education Department Fund of China(JJKH20240920KJ)。

摘　　要：Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced resolution.This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)network.To improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised networks.To reconstruct the PSF trained by the network in the optical path,a phase mask is introduced.It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network.The results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.

关 键 词：Techniques:image processing Telescopes Stars:imaging 

分 类 号：P111[天文地球—天文学]