Asset Details
Do you wish to reserve the book?
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging
Do you wish to request the book?
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging
2024
Overview
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 modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform.
Content-aware frame interpolation (CAFI) improves the temporal resolution in time-lapse imaging by accurately predicting images in between image pairs. By allowing fewer frames to be imaged, CAFI also enables gentler live-cell imaging.
Publisher
Nature Publishing Group US,Nature Publishing Group
