# Coordinate-based neural representations for computational adaptive optics in widefield microscopy

arXiv: https://arxiv.org/abs/2307.03812 · HTML: https://mlsanity.com/paper/2307.03812/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2023.

## Built on by (most cited first, 10 of 10)

- [Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives](https://mlsanity.com/paper/2505.15222/) (2025)
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- [Learning neural representations for X-ray ptychography reconstruction with unknown probes](https://mlsanity.com/paper/2509.04402/) (2025)
- [WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities](https://mlsanity.com/paper/2412.09775/) (2024)
- [Fourier-Based 3D Multistage Transformer for Aberration Correction in Multicellular Specimens](https://mlsanity.com/paper/2503.12593/) (2025)
- [DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror](https://mlsanity.com/paper/2504.14157/) (2025)
- [Physics-Informed Graph Neural Networks for Frequency-Aware Optical Aberration Correction](https://mlsanity.com/paper/2512.05683/) (2025)

## Abstract (arXiv)

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.