Predictive Local Sharpening and Digital Holography

Digital holography and image sharpening have been used increasingly in recent years for wavefront sensing and imaging. Compared to conventional imaging and wavefront sensing techniques, digital holography and image sharpening require significantly fewer and simpler optical components to retrieve the complex field (i.e., both the amplitude and phase) and produce a focused image from an estimate of the phase aberrations present in the imaging system. A drawback for digital holography in real-time applications, such as wavefront sensing for high energy laser systems and high-speed imaging for target tracking systems, is the fact that digital holography and image sharpening are computationally intensive, requiring iterative virtual wavefront propagation to optimize sharpness criteria. Recently, it was shown that minimum variance wavefront prediction can be integrated with digital holography and image sharpening to reduce significantly the large number of costly sharpening iterations required to achieve near-optimal wavefront correction. 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 correction on reduced-dimension subspaces of the complex field in a pupil plane. Results in this paper from wave-optics simulations show that the new subspace method produces results comparable to that from conventional global and local sharpening, and that subspace wavefront estimation and sharpening coupled with wavefront prediction achieves order-of-magnitude increases in processing speed.