KernelGAN: Blind Super Resolution Kernel Estimation using an Internal-GAN
1. 介绍 论文的出发点是要发掘patch上的自相关分布，通过生成器G生成LR downsample后的版本和LR自己相应的patch在分布上更相似（通过D网络判别学到），从而学习出LR的降质过程，和ZSSR一样，这个工作也是在单图上进行训练和测试，不同的是，这里像ZSSR一样使用bicubic作为默认降质，而是利用GAN利用自身分布去学习自己降质的过程。其中G网络用来学习降采样，当G网络被训练好
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相关文章
- 1. Real-World Super-Resolution via Kernel Estimation and Noise Injection ---2020.08.10
- 2. Unpaired Image Super-Resolution using Pseudo-Supervision
- 3. -Zero-Shot-- Super-Resolution using Deep Internal Learning
- 4. CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
- 5. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural
- 6. 论文翻译：2017_Audio super resolution using neural networks
- 7. (NIPS2020)Unfolding the Alternating Optimization for Blind Super Resolution 笔记
- 8. An Attention-Based Approach for Single Image Super Resolution
- 9. 《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》
- 10. Image Super Resolution Using Conventional Neural Network (SRCNN)