Fast algorithms for model-based imaging through turbulence

Abstract

Digital holography (DH) systems have the potential to perform single-shot imaging through deep turbulence by incorporating emerging algorithms, such as model-based iterative reconstruction (MBIR), that jointly estimate both the phase-errors and speckle-free image. However, the high computational cost of MBIR poses a challenge for use in practical applications. In this paper, we propose a method that makes MBIR feasible for real-time DH systems. Our method uses surrogate optimization techniques to simplify and speed up the reflectance and phase-error updates in MBIR. Further, our method accelerates computation of the surrogate-updates by leveraging cache-prefetching and SIMD vector processing units on each CPU core. We analyze the convergence and real CPU time of our method using simulated data sets, and demonstrate its dramatic speedup over the original MBIR approach.