Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

ABSTRACT: Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to di

SUBMITTER: Guo M 

PROVIDER: S-EPMC10659418 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature