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http://dx.doi.org/10.3807/COPP.2022.6.3.260

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)
Publication Information
Current Optics and Photonics / v.6, no.3, 2022 , pp. 260-269 More about this Journal
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
Deep learning; Image reconstruction; Optical imaging; Optical interferometry; Photonic integrated circuits;
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Times Cited By KSCI : 2  (Citation Analysis)
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