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A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions.
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms current state-of-the-art SR models, both quantitatively and qualitatively.
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