Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Image Reconstruction Based on Deep Learning for the SPIDER Optical Interferometric System

  • Sun, Yan (School of Mechanical and Aerospace Engineering, Jilin University) ;
  • Liu, Chunling (Meteorological Service Center, Henan Meteorological Administration) ;
  • Ma, Hongliu (School of Mechanical and Aerospace Engineering, Jilin University) ;
  • Zhang, Wang (School of Mechanical and Aerospace Engineering, Jilin University)
  • Received : 2021.12.20
  • Accepted : 2022.03.31
  • Published : 2022.06.25
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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.

Keywords

Acknowledgement

The authors would like to thank the Editor in Chief, the Associate Editor, and the reviewers for their insightful comments and suggestions.

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