DICTIONARY-BASED MULTIPLE FRAME VIDEO SUPER-RESOLUTION

In this paper, we propose a multiple-frame super-resolution (SR) algorithm based on dictionary learning and motion estimation. We adopt the use of multiple bilevel dictionaries which have also been used for single-frame SR. Multiple frames compensated through sub-pixel motion are considered. By simultaneously solving for a batch of patches from multiple frames, the proposed multiple-frame SR algorithm improves over single frame SR. We also propose a novel dictionary learning algorithm based on which dictionaries are trained from consecutive video frames, rather than still images or individual video frames, which further improves the performance of the developed video SR algorithm. Extensive experimental comparisons with state-of-the-art SR algorithms verifies the effectiveness of our proposed multiple-frame SR approach.