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We present a simple and effective deep convolutional neural network (CNN)model for video deblurring. The proposed algorithm mainly consists of opticalflow estimation from intermediate latent frames and latent frame restorationsteps. It first develops a deep CNN model to estimate optical flow fromintermediate latent frames and then restores the latent frames based on theestimated optical flow. To better explore the temporal information from videos,we develop a temporal sharpness prior to constrain the deep CNN model to helpthe latent frame restoration. We develop an effective cascaded trainingapproach and jointly train the proposed CNN model in an end-to-end manner. Weshow that exploring the domain knowledge of video deblurring is able to makethe deep CNN model more compact and efficient. Extensive experimental resultsshow that the proposed algorithm performs favorably against state-of-the-artmethods on the benchmark datasets as well as real-world videos.

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior