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We propose a novel framework to generate clean video frames from a singlemotion-blurred image. While a broad range of literature focuses on recovering asingle image from a blurred image, in this work, we tackle a more challengingtask i.e. video restoration from a blurred image. We formulate videorestoration from a single blurred image as an inverse problem by setting cleanimage sequence and their respective motion as latent factors, and the blurredimage as an observation. Our framework is based on an encoder-decoder structurewith spatial transformer network modules to restore a video sequence and itsunderlying motion in an end-to-end manner. We design a loss function andregularizers with complementary properties to stabilize the training andanalyze variant models of the proposed network. The effectiveness andtransferability of our network are highlighted through a large set ofexperiments on two different types of datasets: camera rotation blurs generatedfrom panorama scenes and dynamic motion blurs in high speed videos.

Restoration of Video Frames from a Single Blurred Image with Motion Understanding