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Found 5 papers, 4 papers with code
Local-Global Fusion Network for Video Super-Resolution
The results on benchmark datasets of our LGFN are presented on https://github. com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.
Ranked #4 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i. e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner.
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution
Firstly, instead of producing a single score to discriminate images between real and fake, we propose a variant, called Fine-grained Attention Generative Adversarial Network for image super-resolution (FASRGAN), to discriminate each pixel between real and fake.
Deformable Non-local Network for Video Super-Resolution
The video super-resolution (VSR) task aims to restore a high-resolution (HR) video frame by using its corresponding low-resolution (LR) frame and multiple neighboring frames.
