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Deep Neural Networks are increasingly used in video frame interpolation taskssuch as frame rate changes as well as generating fake face videos. Our projectaims to apply recent advances in Deep video interpolation to increase thetemporal resolution of fluorescent microscopy time-lapse movies. To ourknowledge, there is no previous work that uses Convolutional Neural Networks(CNN) to generate frames between two consecutive microscopy images. We proposea fully convolutional autoencoder network that takes as input two images andgenerates upto seven intermediate images. Our architecture has two encoderseach with a skip connection to a single decoder. We evaluate the performance ofseveral variants of our model that differ in network architecture and lossfunction. Our best model out-performs state of the art video frameinterpolation algorithms. We also show qualitative and quantitative comparisonswith state-of-the-art video frame interpolation algorithms. We believe deepvideo interpolation represents a new approach to improve the time-resolution offluorescent microscopy.

W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos