Sparse Representation-Based Multiple Frame Video Super-Resolution

作者：Dai Qiqin^*; Yoo Seunghwan; Kappeler Armin; Katsaggelos Aggelos K

来源：IEEE Transactions on Image Processing, 2017, 26(2): 765-781.

DOI：10.1109/TIP.2016.2631339

• Video super-resolution
• dictionary learning
• sparse coding
• optical flow
• motion estimation

In this paper, we propose two multiple-frame super-resolution (SR) algorithms based on dictionary learning (DL) and motion estimation. First, we adopt the use of video bilevel DL, which has been used for single-frame SR. It is extended to multiple frames by using motion estimation with sub-pixel accuracy. We propose a batch and a temporally recursive multi-frame SR algorithm, which improves over single-frame SR. Finally, we propose a novel DL algorithm utilizing consecutive video frames, rather than still images or individual video frames, which further improves the performance of the video SR algorithms. Extensive experimental comparisons with the state-of-the-art SR algorithms verify the effectiveness of our proposed multiple-frame video SR approach.

• 出版日期2017-2
• 单位西北大学