Wave front sensor-less adaptive optics based on sharpness metrics for particle image velocimetry
M. Teich, J. Grottke, H. Radner, L. B\"Uttner, and J.W. Czarske

TL;DR
This paper presents a wave front sensor-less adaptive optics method using sharpness metrics and iterative optimization with a deformable mirror to improve particle image velocimetry accuracy in fluid flow measurements.
Contribution
It introduces a novel wave front correction technique that eliminates the need for a wave front sensor in PIV systems, enhancing measurement accuracy.
Findings
Effective wave front correction without WFS demonstrated.
Reduction in measurement uncertainties achieved.
Multiple sharpness metrics validated for optimization.
Abstract
Optical distortions can significantly deteriorate the measurement accuracy in imaging systems. Such distortions can occur at fluctuating phase boundaries as well as multiple-phase flows and result from the accompanied refractive index changes. Due to multiple reflexes arising from a fluid flow setup, the usage of a wave front sensor (WFS) can be hindered. In this work we outline a wave front sensor-less approach which includes iterative aberration correction with a fast deformable mirror (DM). A combination of sharpness metric (SM) image evaluation and iterative optimization is demonstrated. The SM was measured for each image while adjusting seven Zernike modes (after Noll index enumeration) in their amplitude. The SM is used as an indicator for wave front aberrations without using a wave front sensor to correct wave front distortions that are generated by the DM. The proposed method…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdaptive optics and wavefront sensing · Advanced Vision and Imaging · Optical Coherence Tomography Applications
