2024
WACV
WACV 2024
Deep Optics for Optomechanical Control Policy Design
Abstract
An emerging class of Fizeau optical telescopes have the potential to upend prior cost scaling models, substantially improving the angular resolution and contrast attainable by ground-based astronomical instruments. However, this design introduces a challenging visual control problem that must be solved to compensate for wavefront aberrations induced by the flexible substructure it employs. We subvert this problem with a deep optics approach to policy design and image recovery that exploits, rather than corrects, aberrations to obtain domain-specific object recovery performance exceeding that of more costly filled aperture designs.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Security & Privacy, Speech & Audio