Acknowledgement
The authors thank the Editor-in-Chief, the reviewers, the School of Mechanical and Aerospace Engineering of Jilin University and College of Mechanical Engineering and Automation of Fuzhou University for this work.
References
- R. P. Scott, T. Su, C. Ogden, S. T. Thurman, R. L. Kendrick, A. Duncan, R. Yu, and S. J. B. Yoo, "Demonstration of a photonic integrated circuit for multi-baseline interferometric imaging," in Proc. IEEE Photonics Conference (San Diego, USA, Oct. 12-16, 2014), pp. 1-2.
- T. Su, R. P. Scott, C. Ogden, S. T. Thurman, R. L. Kendrick, A. Duncan, S. J. B. Yoo, "Experimental demonstration of interferometric imaging using photonic integrated circuits," Opt. Express 25, 12653-12665 (2017).
- C. Li, W. Yin, H. Jiang, and Y. Zhang, "An efficient augmented Lagrangian method with applications to total variation minimization," Comput. Optim. Appl. 56, 507-530 (2013).
- L. Pratley, J. D. McEwen, M. d'Avezac, R. E. Carrillo, A. Onose, and Y. Wiaux, "Robust sparse image reconstruction of radio interferometric observations with PURIFY," Mon. Not. R. Astron. Soc. 473, 1038-1058 (2018).
- W. P. Gao, X. R. Wang, L. Ma, Y. Yuan, and D. F. Guo, "Quantitative analysis of segmented planar imaging quality based on hierarchical multistage sampling lens array," Opt. Express 27, 7955-7967 (2019).
- W. Zhang, H. Ma, and K. Huang, "Spatial frequency coverage and image reconstruction for photonic integrated interferometric imaging system," Curr. Opt. Photonics 5, 606-616 (2021).
- S. T. Thurman, R. L. Kendrick, A. Duncan, D. Wuchenich, and C. Ogden, "System design for a SPIDER imager," in Frontiers in Optics (Optical Society of America, 2015), paper FM3E.3.
- Y. Sun, C. Liu, H. Ma, and W. Zhang, "Image reconstruction based on deep learning for the SPIDER Optical Interferometric System," Curr. Opt. Photonics 6, 260-269 (2022).
- Q. Chu, Y. Shen, M. Yuan, and M. Gong, "Numerical simulation and optimal design of segmented planar imaging detector for electro-optical reconnaissance," Opt. Commun. 405, 288-296 (2017).
- G.-M. Lv, Q. Li, Y.-T. Chen, H.-J. Feng, Z.-H. Xu, and J. Mu, "An improved scheme and numerical simulation of segmented planar imaging detector for electro-optical reconnaissance," Opt. Rev. 26, 664-675 (2019).
- J. Yong, Z. Feng, Z. Wu, S. Ye, M. Li, J. Wu, and C. Cao, "Photonic integrated interferometric imaging based on main and auxiliary nested microlens arrays," Opt. Express 30, 29472-29484 (2022).
- Q. Yu, B. Ge, Y. Li, Y. Yue, F. Chen, and S. Sun, "System design for a "checkerboard" imager," Appl. Opt. 57, 10218-10223 (2018).
- T. Su, G. Liu, K. E. Badham, S. T. Thurman, R. L. Kendrick, A. Duncan, D. Wuchenich, C. Ogden, G. Chriqui, S. Feng, J. Chun, W. Lai, and S. J. B. Yoo, "Interferometric imaging using Si3N4 photonic integrated circuits for a SPIDER imager," Opt. Express 26, 12801-12812 (2018).
- G. Liu, D. Wen, Z. Song, and T. Jiang, "System design of an optical interferometer based on compressive sensing: An update," Opt. Express 28, 19349-19361 (2020).
- W. Gao, Y. Yuan, X. Wang, L. Ma, Z. Zhao, and H. Yuan, "Quantitative analysis and optimization design of the segmented planar integrated optical imaging system based on an inhomogeneous multistage sampling lens array," Opt. Express 29, 11869-11884 (2021).
- S. Nah, T. H. Kim, and K. M. Lee, "Deep multi-scale convolutional neural network for dynamic scene deblurring," arXiv:1612.02177v2 (2018).
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," arXiv:1512.03385v1 (2015).
- J. Liu, W. Zhang, Y. Tang, J. Tang, and G. Wu, "Residual feature aggregation network for image super-resolution," in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition-CVPR (Seattle, WA, USA, Jun. 13-19, 2020), pp. 2356-2365.
- K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Trans. Image Process 16, 2080-2095 (2007).
- S. W. Zamir, A. Arora, A. Gupta, S. Khan, G. Sun, F. S. Khan, F. Zhu, L. Shao, G.-S. Xia, and X. Bai, "iSAID: A large-scale dataset for instance segmentation in aerial images," in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition-CVPR Workshops (Long Beach, CA, USA, Jun. 15-20, 2019), pp. 28-37.
- K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, "ReconNet: Non-iterative reconstruction of images from compressively sensed measurements," in Proc. IEEE Conference on Computer Vision and Pattern Recognition-CVPR (Las Vegas, USA, Jun. 26-Jul. 1, 2016), pp. 449-458.