Fast video super-resolution via approximate nearest neighbor search
Image super-resolution has gained much attention in these years, while video super-resolution remains almost unchanged. In this paper, we propose a fast super-resolution method for video. We exploit recent development of learning-based technique that achieves state-of-the-art in accuracy and efficiency for image super-resolution. We leverage the temporal coherency of video contents to approximate the nearest neighbor search in learning-based SR. Experimental results show that our method is able to produce visually similar or better results while being 20 times faster than baseline frame-by-frame fast image SR and being orders of magnitude faster than complex optimization-based video SR.