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Deformable 3D Convolution for Video Super-Resolution
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DOI：10.1109/LSP.2020.3013518.png)
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Abstract
En 中文
The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing video SR methods since spatial feature extraction and temporal motion compensation are usually performed sequentially. In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR. Specifically, we introduce deformable 3D convolution (D3D) to integrate deformable convolution with 3D convolution, obtaining both superior spatio-temporal modeling capability and motion-aware modeling flexibility. Extensive experiments have demonstrated the effectiveness of D3D in exploiting spatio-temporal information. Comparative results show that our network achieves state-of-the-art SR performance. Code is available at: https://github.com/XinyiYing/D3Dnet.
Keywords:
Convolution
Three-dimensional displays
Motion compensation
Feature extraction
Image resolution
Signal resolution
Solid modeling
Video super-resolution
deformable convolution
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