Video super-resolution with scene-specific priors

In this paper, we propose a method to improve the spatial resolution of video sequences. Our approach is inspired by previous image hallucination work [12]. There are two main contributions of the proposed method. First, the information from cameras with different spatial-temporal resolutions is combined in our framework. This is achieved by constructing training dictionary using the high resolution images captured by still camera and the low resolution video is enhanced via searching in this scene-specific database. Since the dictionary is customized to a particular scene instead of built from arbitrary images, it has fewer but more representative samples. Second, we enforce the spatio-temporal constraints using the conditional random field (CRF) and the problem of video super-resolution is posed as finding the high resolution video that maximizes the conditional probability. We apply the algorithm to video sequences taken from different scenes and the results demonstrate that our approach can synthesize high quality super-resolution videos.