Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network. (arXiv:2211.10563v1 [cs.CV])

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive
performance on image super-resolution tasks. However, these deep learning-based
super-resolution methods perform poorly in real-world super-resolution tasks,
where the paired high-resolution and low-resolution images are unavailable and
the low-resolution images are degraded by complicated and unknown kernels. To
break these limitations, we propose the Unsupervised Bi-directional Cycle
Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN),
which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network
(UBCDTN) and the Semantic Encoder guided Super Resolution …

arxiv generative adversarial network image network super resolution transfer transfer learning unsupervised