LAUSR

Video deblurring is a challenging problem as the blur in videos is usually caused by camera shake, object motion, depth variation, etc. Existing methods usually impose handcrafted image priors or use end-to-end trainable networks to solve this problem. However, using image priors usually leads to highly non-convex problems while directly using end-to-end trainable networks in a regression generates over-smoothes details in the restored images. In this article, we explore the sharpness features from exemplars to help the blur removal and details restoration. We first estimate optical flow to explore the temporal information which can help to make full use of neighboring information. Then, we develop an encoder and decoder network and explore the sharpness features from exemplars to guide the network for better image restoration. We train the proposed algorithm in an end-to-end manner and show that using sharpness features from exemplars can help blur removal and details restoration. Both quantitative and qualitative evaluations demonstrate that our method performs favorably against state-of-the-art approaches on the benchmark video deblurring datasets and real-world images.