A Deep Learning Framework for Video Temporal Super-Resolution

Abstract

This thesis introduces a deep learning approach for the problem of video temporal super-resolution. Specifically, a network architecture and training schemes are proposed to produce an output video as it was captured using half the exposure time of the camera. By the recursive application of this model, the temporal resolution is further expanded by a factor of 4, 8, ..., 2^N. The only assumption is made is that the input video has been recorded with a camera with the shutter fully open. In extensive experiments with real data, it is demonstrated that this methodology intrinsically handles the problem of joint deblurring and frame interpolation. Moreover, visual results show that the recursive mechanism makes frames sharper and sharper in every step. Nevertheless, it fails at generating temporally smooth videos.