DOI QR코드

DOI QR Code

Improving the Capture-range Problem in Phase-diversity Phase Retrieval for Laser-wavefront Measurement Using Geometrical-optics Initial Estimates

  • Li, Li Jie (School of Opto-electronic Engineering, Changchun University of Science and Technology) ;
  • Jing, Wen Bo (School of Opto-electronic Engineering, Changchun University of Science and Technology) ;
  • Shen, Wen (Department of Management Engineering, Jilin Communications Polytechnic) ;
  • Weng, Yue (Chengdu Branch of Software Platform R&D Department, Dahua Technology) ;
  • Huang, Bing Kun (School of Opto-electronic Engineering, Changchun University of Science and Technology) ;
  • Feng, Xuan (School of Opto-electronic Engineering, Changchun University of Science and Technology)
  • 투고 : 2022.04.26
  • 심사 : 2022.07.18
  • 발행 : 2022.10.25

초록

To overcome the capture-range problem in phase-diversity phase retrieval (PDPR), a geometrical-optics initial-estimate method is proposed to avoid a local minimum and to improve the accuracy of laser-wavefront measurement. We calculate the low-order aberrations through the geometrical-optics model, which is based on the two spot images in the propagation path of the laser, and provide it as a starting guess for the PDPR algorithm. Simulations show that this improves the accuracy of wavefront recovery by 62.17% compared to other initial values, and the iteration time with our method is reduced by 28.96%. That is, this approach can solve the capture-range problem.

키워드

과제정보

111 project of China (D21009); Ministry of Science and Technology of the People's Republic of China (2018YFB1107600); Jilin Scientific and Technological Development Program (20160204009GX, 20170204014GX).

참고문헌

  1. V. Y. Zavalova and A. V. Kudryashov, "Shack-Hartmann wavefront sensor for laser beam analysis," Proc. SPIE 4493, 277-284 (2002).
  2. R. G. Paxman, T. J. Schulz, and J. R. Fienup, "Joint estimation of object and aberrations by using phase diversity," J. Opt. Soc. Am. A 9, 1072-1085 (1992). https://doi.org/10.1364/josaa.9.001072
  3. L. M. Mugnier, A. Blanc, and J. Idier, "Phase diversity: a technique for wave-front sensing and for diffraction-limited imaging," Adv. Imaging Electron Phys. 141, 1-76 (2006). https://doi.org/10.1016/S1076-5670(05)41001-0
  4. A. S. Jurling and J. R. Fienup, "Improved method for solving the capture range problem in focus-diverse phase retrieval for segmented systems," in Frontiers in Optics 2010/Laser Science XXVI (Optica Publishing Group, 2010), paper FWV4.
  5. D. B. Moore and J. R. Fienup, "Extending the capture range of phase retrieval through random starting parameters," in Frontiers in Optics 2014 (Optica Publishing Group, 2014), paper FTu2C.2.
  6. S. W. Paine and J. R. Fienup, "Extending capture range for piston retrieval in segmented systems," Appl. Opt. 56, 9186-9192 (2017). https://doi.org/10.1364/AO.56.009186
  7. S. T. Thurman, "Method of obtaining wavefront slope data from through-focus point spread function measurements," J. Opt. Soc. Am. A 28, 1-7 (2011). https://doi.org/10.1364/JOSA.28.000001
  8. A. S. Jurling, "Advances in algorithms for image based wave-front sensing," Ph. D. Thesis, University of Rochester, NY (2015).
  9. A. S. Jurling and J. R. Fienup, "Extended capture range for focus-diverse phase retrieval in segmented aperture systems using geometrical optics," J. Opt. Soc. Am. A 31, 661-666 (2014). https://doi.org/10.1364/JOSAA.31.000661
  10. R. E. Carlisle and D. S. Acton, "Demonstration of extended capture range for James Webb Space Telescope phase retrieval," Appl. Opt. 54, 6454-6460 (2015). https://doi.org/10.1364/AO.54.006454
  11. S. W. Paine and J. R. Fienup, "Overcoming large piston capture range problems in segmented systems using broadband light," in Imaging and Applied Optics 2015 (Optica Publishing Group, 2015), paper AOTh1D.2.
  12. H. Cao, J. Zhang, F. Yang, Q. An, and Y. Wang, "Extending capture range for piston error in segmented primary mirror telescopes based on wavelet support vector machine with improved particle swarm optimization," IEEE Access 8, 111585-111597 (2020). https://doi.org/10.1109/access.2020.3002901
  13. W. Zhao, L. Zhang, Y. Zhao, L. Dong, and M. Hui, "High-accuracy piston error measurement with a large capture range based on coherent diffraction," Proc. SPIE 11056, 110563B (2019).
  14. P. G. Zhang, C. L. Yang, Z. H. Xu, Z. L. Cao, Q. Q. Mu, and L. Xuan, "Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval," Opt. Express 24, 25704-25717 (2016). https://doi.org/10.1364/OE.24.025704
  15. G. Ju, X. Qi, H. Ma, and C. Yan, "Feature-based phase retrieval wavefront sensing approach using machine learning," Opt. Express 26, 31767-31783 (2018). https://doi.org/10.1364/oe.26.031767
  16. S. W. Paine and J. R. Fienup, "Machine learning for improved image-based wavefront sensing," Opt. Lett. 43, 1235-1238 (2018). https://doi.org/10.1364/OL.43.001235
  17. S. W. Paine, "Expanding the capture range of image-based wavefront sensing problems," Ph. D. Thesis, University of Rochester, NY (2019), p. 151.
  18. C. Weinberger, F. Guzman, and E. Vera, "Improved training for the deep learning wavefront sensor," Proc. SPIE 11448, 114484G (2020).
  19. S. W. Paine and J. R. Fienup, "Machine learning for avoiding stagnation in image-based wavefront sensing," Proc. SPIE 10980, 109800T (2019).
  20. J. C. Wyant and K. Creath, "Basic wavefront aberration theory for optical metrology," in Applied Optics and Optical Engineering Series, R. R. Shannon and J. C. Wyant, Eds. (Academic Press, USA, 1992), Volume. 11, p. 28.
  21. D. C. Liu and J. Nocedal, "On the limited memory BFGS method for large scale optimization," Math. Program. 45, 503-528 (1989). https://doi.org/10.1007/BF01589116