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AssigneeThe Hong Kong University of Science and Technology
InventorsShueng Han Gary Chan, Song Wen
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A VSR approach with temporal consistency using generative adversarial networks (VistGAN) that requires only the training HR video sequence to generate the HR/LR video frame pairs, instead of the pre-artificial-synthesized HR/LR video frame pairs, for training. By this unsupervised learning method, the encoder degrades the input HR video frames of a training HR video sequence to their LR counterparts, and the decoder seeks to recover the original HR video frames from the LR video frames. To improve the temporal consistency the unsupervised learning method provides a sliding window that explores the temporal correlation in both HR and LR domains. It keeps the temporal consistent and also fully utilizes high-frequency details from the last-generated reconstructed HR video frame.
An apparatus for video super-resolution (VSR) using generative adversarial networks (GAN), comprising:
A method for training an apparatus for video super-resolution (VSR) using generative adversarial networks (GAN), comprising:
An apparatus for video super-resolution (VSR) using generative adversarial networks (GAN), comprising:
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