![]() ![]() We benchmark CAFI’s performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disk confocal and confocal brightfield microscopy). We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. ![]() Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |