An Efficient Video Super-Resolution Approach based on Sparse Representation

Block-wise super-resolution methods, and in particular sparse representation based approaches, often focus on spatial upsampling of still images. Applying such models to videos is an extremely time consuming process due to the expensive sparse coding process for every block of each frame, and the conventional exhaustive overlapping blocks processing for reducing the blocking artifacts. In this paper, we introduce an approach that enables us to skip the sparse representation process for the static portions of a video by predicting the high resolution blocks from previously super-resolved frames, exploiting the significant amount of correlation between the adjacent frames in a video. Our approach is also enhanced by adaptive in-loop filters for removing the blocking and pixel-wise artifacts, replacing the overlapping blocks structure of super-resolution. Our method provides comparable results in terms of image quality with respect to the state of the art, and can reduce the processing time of the super-resolution methods by 63% in average. It enables the block based super-resolution methods to be applied in upsampling of videos to very high resolutions with a reasonable processing time.