Open AccessSubspace wavefront estimation using image sharpening and predictive dynamic digital holography
Author(s) -
Sennan Sulaiman,
Steve Gibson,
Mark F. Spencer
Publication year - 2020
Publication title -
journal of the optical society of america. a, optics, image science, and vision./journal of the optical society of america. a, online
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.803
H-Index - 158
eISSN - 1520-8532
pISSN - 1084-7529
DOI - 10.1364/josaa.393862
Subject(s) - sharpening , wavefront , computer science , subspace topology , digital holography , deformable mirror , holography , wavefront sensor , optics , artificial intelligence , computer vision , algorithm , physics
Image sharpening algorithms used for phase retrieval to reconstruct images in digital holography are computationally intensive, requiring iterative virtual wavefront propagation and hill-climbing algorithms to optimize sharpness criteria. Recently, it was shown that minimum-variance wavefront prediction can be integrated with digital holography and image sharpening to significantly reduce the large number of costly sharpening iterations normally required to achieve near-optimal wavefront estimation [J. Opt. Soc. Am. A35, 923 (2018)JOAOD60740-323210.1364/JOSAA.35.000923]. This paper demonstrates further gains in computational efficiency with a new subspace sharpening method in conjunction with predictive dynamic digital holography for real-time applications. The method sharpens local regions of interest in an image plane by parallel independent wavefront estimation on reduced-dimension subspaces of the complex field in a pupil plane. Through wave-optics simulations, this paper shows that the new subspace method produces results comparable to those of conventional global and local sharpening, and that subspace wavefront estimation and sharpening coupled with wavefront prediction achieve orders-of-magnitude increases in processing speed.