RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution
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Abstract (arXiv)
Recently, video super resolution (VSR) has become a very impactful task in the area of Computer Vision due to its various applications. In this paper, we propose Recurrent Back-Projection Generative Adversarial Network (RBPGAN) for VSR in an attempt to generate temporally coherent solutions while preserving spatial details. RBPGAN integrates two state-of-the-art models to get the best in both worlds without compromising the accuracy of produced video. The generator of the model is inspired by RBPN system, while the discriminator is inspired by TecoGAN. We also utilize Ping-Pong loss to increase temporal consistency over time. Our contribution together results in a model that outperforms earlier work in terms of temporally consistent details, as we will demonstrate qualitatively and quantitatively using different datasets.
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References · 9
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2018Self-Attention Generative Adversarial Networkscited by 809
- 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 492
- 2018Assessing Generative Models via Precision and Recallcited by 401
- 2019Recurrent Back-Projection Network for Video Super-Resolutioncited by 147
- 2020Unpaired Motion Style Transfer from Video to Animationcited by 94
- 2018Avoiding Latent Variable Collapse With Generative Skip Modelscited by 47
- 2018Generative Adversarial Networks and Perceptual Losses for Video Super-Resolutioncited by 30
- 2018Learning Temporal Coherence via Self-Supervision for GAN-based Video Generationcited by 18
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