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Video Superresolution via Motion Compensation and Deep Residual Learning | IEEE Journals & Magazine | IEEE Xplore

Video Superresolution via Motion Compensation and Deep Residual Learning


Abstract:

Video superresolution (SR) techniques are of essential usages for high-resolution display devices due to the current lack of high-resolution videos. Although many algorit...Show More

Abstract:

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 named 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.
Published in: IEEE Transactions on Computational Imaging ( Volume: 3, Issue: 4, December 2017)
Page(s): 749 - 762
Date of Publication: 17 February 2017

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