Journal
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
Volume 3, Issue 4, Pages 749-762Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCI.2017.2671360
Keywords
Convolutional neural networks (CNNs); deep residual learning; multi-frame super-resolution; video superresolution
Funding
- National Natural Science Foundation of China [61472393]
Ask authors/readers for more resources
Video superresolution (SR) techniques are of essential usages for high-resolution display devices due to the current lack of high-resolution videos. Although many algorithms have been proposed, video SR still remains a very challenging inverse problem under different conditions. In this paper, we propose a new method for video SR name motion compensation and residual net (MCResNet). We use optical flow algorithm for motion estimation and motion compensation as a preprocessing step. Then, we employ a novel deep residual convolutional neural network (CNN) to predict a high-resolution image using multiple motion compensated observations. The new residual CNN model preserves the low-frequency contents and facilitates the restoration of high-frequency details. Our method is able to handle large and complex motions adaptively. Extensive experimental results validate that our proposed method outperforms state-of-the-art single-image-based and multi-frame-based algorithms for video SR quantitatively and qualitatively.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available