Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Image Reconstruction Method for Photonic Integrated Interferometric Imaging Based on Deep Learning

  • Qianchen Xu (School of Mechanical and Aerospace Engineering, Jilin University) ;
  • Weijie Chang (College of Mechanical Engineering and Automation, Fuzhou University) ;
  • Feng Huang (College of Mechanical Engineering and Automation, Fuzhou University) ;
  • Wang Zhang (School of Mechanical and Aerospace Engineering, Jilin University)
  • Received : 2024.04.04
  • Accepted : 2024.07.22
  • Published : 2024.08.25
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Abstract

An image reconstruction algorithm is vital for the image quality of a photonic integrated interferometric imaging (PIII) system. However, image reconstruction algorithms have limitations that always lead to degraded image reconstruction. In this paper, a novel image reconstruction algorithm based on deep learning is proposed. Firstly, the principle of optical signal transmission through the PIII system is investigated. A dataset suitable for image reconstruction of the PIII system is constructed. Key aspects such as model and loss functions are compared and constructed to solve the problem of image blurring and noise influence. By comparing it with other algorithms, the proposed algorithm is verified to have good reconstruction results not only qualitatively but also quantitatively.

Keywords

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

  1. 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.
  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). https://doi.org/10.1364/OE.25.012653
  3. 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). https://doi.org/10.1007/s10589-013-9576-1
  4. 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). https://doi.org/10.1093/mnras/stx2237
  5. 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). https://doi.org/10.1364/OE.27.007955
  6. 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). https://doi.org/10.3807/COPP.2021.5.6.606
  7. 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.
  8. 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). https://doi.org/10.3807/COPP.2022.6.3.260
  9. 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). https://doi.org/10.1016/j.optcom.2017.08.021
  10. 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). https://doi.org/10.1007/s10043-019-00548-w
  11. 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). https://doi.org/10.1364/OE.463504
  12. 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). https://doi.org/10.1364/AO.57.010218
  13. 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). https://doi.org/10.1364/OE.26.012801
  14. 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). https://doi.org/10.1364/OE.394130
  15. 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). https://doi.org/10.1364/OE.421298
  16. S. Nah, T. H. Kim, and K. M. Lee, "Deep multi-scale convolutional neural network for dynamic scene deblurring," arXiv:1612.02177v2 (2018).
  17. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," arXiv:1512.03385v1 (2015).
  18. 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.
  19. 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). https://doi.org/10.1109/TIP.2007.901238
  20. 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.
  21. 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.