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Electrical Engineering and Systems Science > Image and Video Processing
Title: Coordinate-based neural representations for computational adaptive optics in widefield microscopy
(Submitted on 7 Jul 2023 (v1), last revised 1 May 2024 (this version, v3))
Abstract: Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here, we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single input 3D image stack without the need for external training dataset. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA's performance. Using CoCoA, we demonstrated the first in vivo widefield mouse brain imaging using machine-learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general.
Submission history
From: Iksung Kang [view email][v1] Fri, 7 Jul 2023 19:36:24 GMT (13258kb)
[v2] Thu, 25 Apr 2024 04:49:04 GMT (13290kb)
[v3] Wed, 1 May 2024 23:31:41 GMT (32032kb)
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