W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos
This work addresses the need for higher time-resolution in cellular microscopy videos, which is incremental as it adapts existing video interpolation methods to a new domain.
The authors tackled the problem of increasing temporal resolution in fluorescent microscopy videos by applying deep neural networks for video frame interpolation, achieving state-of-the-art performance with a model that generates up to seven intermediate images between two consecutive frames.
Deep Neural Networks are increasingly used in video frame interpolation tasks such as frame rate changes as well as generating fake face videos. Our project aims to apply recent advances in Deep video interpolation to increase the temporal resolution of fluorescent microscopy time-lapse movies. To our knowledge, there is no previous work that uses Convolutional Neural Networks (CNN) to generate frames between two consecutive microscopy images. We propose a fully convolutional autoencoder network that takes as input two images and generates upto seven intermediate images. Our architecture has two encoders each with a skip connection to a single decoder. We evaluate the performance of several variants of our model that differ in network architecture and loss function. Our best model out-performs state of the art video frame interpolation algorithms. We also show qualitative and quantitative comparisons with state-of-the-art video frame interpolation algorithms. We believe deep video interpolation represents a new approach to improve the time-resolution of fluorescent microscopy.