Cascaded Deep Video Deblurring Using Temporal Sharpness Prior

and Technology (a) Input frame (b) Kim and Lee [12] (c) STFAN [32] (d) EDVR [27] (e) Ours (f) Sharpness prior of (a) Figure 1. Deblurred result on a real challenging video. Our algorithm is motivated by the success of variational model-based methods. It explores sharpness pixels from adjacent frames by a temporal sharpness prior (see (f)) and restores sharp videos by a cascaded inference process. As our analysis shows, enforcing the temporal sharpness prior in a deep convolutional neural network (CNN) and learning the deep CNN by a cascaded inference manner can make the deep CNN more compact and thus generate better-deblurred results than both the CNN-based methods [27, 32] and variational model-based method [12].