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      KCI등재 SCIE SCOPUS

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

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      https://www.riss.kr/link?id=A108316511

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      다국어 초록 (Multilingual Abstract)

      To overcome the capture-range problem in phase-diversity phase retrieval (PDPR), a geometricaloptics initial-estimate method is proposed to avoid a local minimum and to improve the accuracy of laser-wavefront measurement. We calculate the low-order ab...

      To overcome the capture-range problem in phase-diversity phase retrieval (PDPR), a geometricaloptics 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.

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      참고문헌 (Reference) 논문관계도

      1 V. Y. Zavalova, "Shack-Hartmann wavefront sensor for laser beam analysis" 4493 : 277-284, 2002

      2 L. M. Mugnier, "Phase diversity : a technique for wave-front sensing and for diffraction-limited imaging" 141 : 1-76, 2006

      3 S. W. Paine, "Overcoming large piston capture range problems in segmented systems using broadband light" Optica Publishing Group 2015

      4 D. C. Liu, "On the limited memory BFGS method for large scale optimization" 45 : 503-528, 1989

      5 S. T. Thurman, "Method of obtaining wavefront slope data from through-focus point spread function measurements" 28 : 1-7, 2011

      6 S. W. Paine, "Machine learning for improved image-based wavefront sensing" 43 : 1235-1238, 2018

      7 S. W. Paine, "Machine learning for avoiding stagnation in image-based wavefront sensing" 10980 : 109800T-, 2019

      8 R. G. Paxman, "Joint estimation of object and aberrations by using phase diversity" 9 : 1072-1085, 1992

      9 C. Weinberger, "Improved training for the deep learning wavefront sensor" 11448 : 114484G-, 2020

      10 A. S. Jurling, "Improved method for solving the capture range problem in focus-diverse phase retrieval for segmented systems" Optica Publishing Group 2010

      1 V. Y. Zavalova, "Shack-Hartmann wavefront sensor for laser beam analysis" 4493 : 277-284, 2002

      2 L. M. Mugnier, "Phase diversity : a technique for wave-front sensing and for diffraction-limited imaging" 141 : 1-76, 2006

      3 S. W. Paine, "Overcoming large piston capture range problems in segmented systems using broadband light" Optica Publishing Group 2015

      4 D. C. Liu, "On the limited memory BFGS method for large scale optimization" 45 : 503-528, 1989

      5 S. T. Thurman, "Method of obtaining wavefront slope data from through-focus point spread function measurements" 28 : 1-7, 2011

      6 S. W. Paine, "Machine learning for improved image-based wavefront sensing" 43 : 1235-1238, 2018

      7 S. W. Paine, "Machine learning for avoiding stagnation in image-based wavefront sensing" 10980 : 109800T-, 2019

      8 R. G. Paxman, "Joint estimation of object and aberrations by using phase diversity" 9 : 1072-1085, 1992

      9 C. Weinberger, "Improved training for the deep learning wavefront sensor" 11448 : 114484G-, 2020

      10 A. S. Jurling, "Improved method for solving the capture range problem in focus-diverse phase retrieval for segmented systems" Optica Publishing Group 2010

      11 P. G. Zhang, "Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval" 24 : 25704-25717, 2016

      12 W. Zhao, "Highaccuracy piston error measurement with a large capture range based on coherent diffraction" 11056 : 110563B-, 2019

      13 G. Ju, "Feature-based phase retrieval wavefront sensing approach using machine learning" 26 : 31767-31783, 2018

      14 D. B. Moore, "Extending the capture range of phase retrieval through random starting parameters" Optica Publishing Group 2014

      15 S. W. Paine, "Extending capture range for piston retrieval in segmented systems" 56 : 9186-9192, 2017

      16 H. Cao, "Extending capture range for piston error in segmented primary mirror telescopes based on wavelet support vector machine with improved particle swarm optimization" 8 : 111585-111597, 2020

      17 A. S. Jurling, "Extended capture range for focus-diverse phase retrieval in segmented aperture systems using geometrical optics" 31 : 661-666, 2014

      18 S. W. Paine, "Expanding the capture range of image-based wavefront sensing problems" University of Rochester 2019

      19 R. E. Carlisle, "Demonstration of extended capture range for James Webb Space Telescope phase retrieval" 54 : 6454-6460, 2015

      20 J. C. Wyant, "Applied Optics and Optical Engineering Series Volume 11" Academic Press 28-, 1992

      21 A. S. Jurling, "Advances in algorithms for image based wavefront sensing" University of Rochester 2015

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