Real Image Super-Resolution using GAN through modeling of LR and HR process
Author:
Affiliation:
1. Institute of AI for Health (AIH),Helmholtz Munich,Germany
2. University of Udine,Department of Mathematics and Computer Science,Italy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9958946/9959140/09959415.pdf?arnumber=9959415
Reference35 articles.
1. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
2. Learning Deep CNN Denoiser Prior for Image Restoration
3. Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
4. Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
5. Cross-scale internal graph neural network for image super-resolution;zhou;NeurIPS,2020
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1. DAE-GAN: Underwater Image Super-Resolution Based on Symmetric Degradation Attention Enhanced Generative Adversarial Network;Symmetry;2024-05-09
2. LBKENet:Lightweight Blur Kernel Estimation Network for Blind Image Super-Resolution;Image Analysis and Processing – ICIAP 2023;2023
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