Image correction and wavefront sensing with a digital holographic sensor using implicit neural representations

Casey J. Pellizzari; Tyler J. Hardy; Mark F. Spencer

doi:10.1117/12.3028360

7 October 2024 Image correction and wavefront sensing with a digital holographic sensor using implicit neural representations

Casey J. Pellizzari, Tyler J. Hardy, Mark F. Spencer

Author Affiliations +

Proceedings Volume 13149, Unconventional Imaging, Sensing, and Adaptive Optics 2024; 131490I (2024) https://doi.org/10.1117/12.3028360
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Using digital holographic (DH) sensors, coupled with iterative computational algorithms we can sense and correct the effects of distributed volume turbulence in DH imagery. These iterative methods minimize a non-convex cost function with respect to the wavefront phase function, modeled as discreet arrays. This approach leads to high-dimensional optimization problems plagued by local minima. The problem is amplified in the presence of challenging conditions, (e.g., high noise, strong turbulence, insufficient data). We investigate using implicit neural representations (INRs) to model atmospheric phase errors in DH data. INRs offer a low-dimensional functional representation, simplifying the optimization problem and allowing us to produce high-quality wavefront estimates and focused images, even in deep-turbulence conditions.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Casey J. Pellizzari, Tyler J. Hardy, and Mark F. Spencer "Image correction and wavefront sensing with a digital holographic sensor using implicit neural representations", Proc. SPIE 13149, Unconventional Imaging, Sensing, and Adaptive Optics 2024, 131490I (7 October 2024); https://doi.org/10.1117/12.3028360

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