Machine learning and neural representations for enhancing phase diversity-based adaptive optics

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Optica Biophotonics Congress 2025. 2025 Apr 21;. doi: 10.1364/NTM.2025.NTu4C.1

Schneider Magdalena

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Abstract

Using phase-diversity wavefront sensing, machine learning-based deformable mirror control, and neural representations of the unknown sample and phase aberration, we demonstrate precise reconstruction of sample structures in fluorescence microscopy, even under severe, high-order aberrations.Full-text article not available; see video presentation

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Machine learning and neural representations for enhancing phase diversity-based adaptive optics

Abstract

Janelia Authors