Image entropy as a metric for iterative optimization of large aberrations

Matthew B. Garvin; Mark T. Gruneisen; Raymond C. Dymale; James R. Rotge

doi:10.1117/12.621918

22 August 2005 Image entropy as a metric for iterative optimization of large aberrations

Matthew B. Garvin, Mark T. Gruneisen, Raymond C. Dymale, James R. Rotge

Proceedings Volume 5894, Advanced Wavefront Control: Methods, Devices, and Applications III; 58940Y (2005) https://doi.org/10.1117/12.621918
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Image metric optimization is an attractive alternative to conventional wavefront sensing for optical systems that are constrained by weight, cost, size, and power consumption and required to operate using light from extended object scenes. For these optical systems, an image metric optimizer must be able to function in the presence of potentially large system aberrations. This paper examines the usefulness of image entropy as a metric for measuring changes in image quality in the presence of large aberrations. In our experiment, we use a liquid-crystal spatial light modulator as a programmable diffractive optic to compensate for roughly 40 waves of peak-to-valley aberration introduced by using a parabolic mirror tilted 5 degrees off the optic axis. The results of our experiment show that image entropy does function well as a metric for measuring changes in image quality for 20 waves of aberration or less. For aberrations greater than 20 waves peak-to-valley the total optical power incident on the camera is a better metric.

Citation Download Citation

Matthew B. Garvin, Mark T. Gruneisen, Raymond C. Dymale, and James R. Rotge "Image entropy as a metric for iterative optimization of large aberrations", Proc. SPIE 5894, Advanced Wavefront Control: Methods, Devices, and Applications III, 58940Y (22 August 2005); https://doi.org/10.1117/12.621918

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