Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging.

ABSTRACT: The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI's performance on 12 different datasets, obtained from four different microscopy

SUBMITTER: Priessner M 

PROVIDER: S-EPMC10864186 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature