Experimental validation of model-based digital holographic imaging using multi-shot data

Timothy Bate; Daniel O'Keefe; Mark F. Spencer; Casey J. Pellizzari

doi:10.1117/12.2633499

4 October 2022 Experimental validation of model-based digital holographic imaging using multi-shot data

Timothy Bate, Daniel O'Keefe, Mark F. Spencer, Casey J. Pellizzari

Proceedings Volume 12239, Unconventional Imaging and Adaptive Optics 2022; 122390D (2022) https://doi.org/10.1117/12.2633499
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Imaging through deep turbulence is a hard and unsolved problem. There have been recent advances toward sensing and correcting moderate turbulence using digital holography (DH). With DH, we use optical heterodyne detection to sense the amplitude and phase of the light reflected from an object. This phase information allows us to digitally back propagate the measured field to estimate and correct distributed-volume aberrations. Recently, we developed a model-based iterative reconstruction (MBIR) algorithm for sensing and correcting atmospheric turbulence using multi-shot DH data (i.e., multiple holographic measurements). Using simulation, we showed the ability to correct deep-turbulence effects, loosely characterized by Rytov numbers greater than 0.75 and isoplanatic angles near the diffraction limited viewing angle. In this work, we demonstrate the validity of our method using laboratory measurements. Our experiments utilized a combination of multiple calibrated Kolmogorov phase screens along the propagation path to emulate distributed-volume turbulence. This controlled laboratory setup allowed us to demonstrate our algorithm’s performance in deep turbulence conditions using real data.

Conference Presentation

Citation Download Citation

Timothy Bate, Daniel O'Keefe, Mark F. Spencer, and Casey J. Pellizzari "Experimental validation of model-based digital holographic imaging using multi-shot data", Proc. SPIE 12239, Unconventional Imaging and Adaptive Optics 2022, 122390D (4 October 2022); https://doi.org/10.1117/12.2633499

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