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. A 35, 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.

Digital holography is often combined with image sharpening for wavefront estimation and correction, and this combination has received recent attention for many imaging and sensing applications. A significant obstacle for digital holography and image sharpening in high-speed real-time applications is the fact that the process is computationally intensive, requiring iterative virtual wavefront propagation and hill-climbing algorithms to optimize sharpness criteria. This paper introduces a method for accelerating dynamic digital holography and image sharpening by wavefront prediction. The approach here integrates optimal state-space prediction filters with digital holography and image sharpening to short-circuit the computationally intensive process of virtual wavefront propagation and sharpness optimization.

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Digital holography has received recent attention for many imaging and sensing applications, including imaging through turbulent and turbid media, adaptive optics, three-dimensional projective display technology and optical tweezing. It holds several advantages over classical methods for wavefront sensing and adaptive-optics correction, chief among these being significantly fewer and simpler optical components. A significant obstacle for digital holography in real-time applications, such as wavefront sensing for high-energy laser systems and high-speed imaging for target tracking, is the fact that digital holography is computationally intensive; it requires iterative virtual wavefront propagation and hill-climbing algorithms to optimize sharpness criteria. This research demonstrates real-time methods for digital holography based on approaches for optimal and adaptive identification, prediction, and control of optical wavefronts. The methods presented integrate minimum-variance wavefront prediction into dynamic digital holography schemes to accelerate the wavefront correction and image sharpening algorithms. Further gains in computational efficiency are demonstrated in this work with a variant of localized sharpening in conjunction with predictive dynamic digital holography for real-time applications. This "subspace correction" method optimizes sharpness of local regions in a detector plane by parallel independent wavefront correction on reduced-dimension subspaces of the complex field in a spectral plane.ii The dissertation of Sennan David Sulaiman is approved.