Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition.

ABSTRACT: Super-resolution fluorescence microscopy methods enable the characterization of nanostructures in living and fixed biological tissues. However, they require the adjustment of multiple imaging parameters while attempting to satisfy conflicting objectives, such as maximizing spatial and temporal resolution while minimizing light exposure. To overcome the limitations imposed by these trade-offs, post-acquisition algorithmic approaches have been proposed for resolution enhancement and image-quality improvement. Here we introduce the task-assisted generative adversarial network (TA-GAN), which incorporates an auxiliary task (for example, segmentation, localization) closely related to the observed biological nanostructure characterization. We evaluate how the TA-GAN improves generative accuracy

SUBMITTER: Bouchard C 

PROVIDER: S-EPMC10442226 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature