Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Image Reconstruction Based on Deep Learning for the SPIDER Optical Interferometric System

Open Access Open Access

Abstract

Segmented planar imaging detector for electro-optical reconnaissance (SPIDER) is an emerging technology for optical imaging. However, this novel detection approach is faced with degraded imaging quality. In this study, a 6 × 6 planar waveguide is used after each lenslet to expand the field of view. The imaging principles of field-plane waveguide structures are described in detail. The local multiple-sampling simulation mode is adopted to process the simulation of the improved imaging system. A novel image-reconstruction algorithm based on deep learning is proposed, which can effectively address the defects in imaging quality that arise during image reconstruction. The proposed algorithm is compared to a conventional algorithm to verify its better reconstruction results. The comparison of different scenarios confirms the suitability of the algorithm to the system in this paper.

© 2022 Optical Society of Korea

PDF Article
More Like This
Deep learning-based image reconstruction for photonic integrated interferometric imaging

Ziran Zhang, Haoying Li, Guomian Lv, Hao Zhou, Huajun Feng, Zhihai Xu, Qi Li, Tingting Jiang, and Yueting Chen
Opt. Express 30(23) 41359-41373 (2022)

Structural design of an improved SPIDER optical system based on a multimode interference coupler

Xiaohan Song, Yong Zuo, Tianjie Zeng, Bohan Si, Xiaobin Hong, and Jian Wu
Opt. Express 31(20) 33704-33718 (2023)

Interferometric imaging using Si3N4 photonic integrated circuits for a SPIDER imager

Tiehui Su, Guangyao Liu, Katherine E Badham, Samuel T. Thurman, Richard L. Kendrick, Alan Duncan, Danielle Wuchenich, Chad Ogden, Guy Chriqui, Shaoqi Feng, Jaeyi Chun, Weicheng Lai, and S. J. B. Yoo
Opt. Express 26(10) 12801-12812 (2018)

Cited By

Optica participates in Crossref's Cited-By Linking service. Citing articles from Optica Publishing Group journals and other participating publishers are listed here.

Alert me when this article is cited.


Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.