Predictive dynamic digital holography

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. 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 to optimize some sharpness criteria. This paper demonstrates real-time methods for digital holography based on approaches developed recently at UCLA for optimal and adaptive identification, prediction, and control of optical wavefronts. The methods presented integrate minimum variance wavefront prediction into digital holography schemes to short-circuit the computationally intensive algorithms for iterative propagation of virtual wavefronts and hill climbing for sharpness optimization.