1. Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
- Author
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Zehan Wang, Andrew Peter Aitken, Jose Caballero, Johannes Totz, Wenzhe Shi, Alejandro Acosta, and Christian Ledig
- Subjects
FOS: Computer and information sciences ,Image fusion ,Motion compensation ,Artificial neural network ,Computer science ,business.industry ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020206 networking & telecommunications ,02 engineering and technology ,Iterative reconstruction ,Convolutional neural network ,Convolution ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Image resolution - Abstract
Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution have been limited to naive or inefficient architectures. In this paper, we introduce spatio-temporal sub-pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real-time speed. Specifically, we discuss the use of early fusion, slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames. We also propose a novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods, relying on a fast multi-resolution spatial transformer module that is end-to-end trainable. These contributions provide both higher accuracy and temporally more consistent videos, which we confirm qualitatively and quantitatively. Relative to single-frame models, spatio-temporal networks can either reduce the computational cost by 30% whilst maintaining the same quality or provide a 0.2dB gain for a similar computational cost. Results on publicly available datasets demonstrate that the proposed algorithms surpass current state-of-the-art performance in both accuracy and efficiency., Comment: Changes: * Uploaded Vid4 results (footnote 1). * Added references [14, 29] as spatial-transformer prior art. * Fixed typos
- Published
- 2017