2020CVPR《Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution》论文阅读
个人对2020cvpr的超分网络DRN的一些理解！ 论文链接：https://arxiv.org/pdf/2003.07018.pdf 源码链接：https://github.com/fengye-lu/DRN-master 论文的出发点：**目前学习从LR到HR的映射关系的超分网络存在两个问题（1）超分是一个“病态”问题，一张LR图像可以对应多个HR图像，就是说其实LR图像可以由多个HR图像降采
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相关文章
- 1. DRN ---Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
- 2. 《Deep Adaptive Inference Networks for Single Image Super-Resolution》论文阅读
- 3. 《Enhanced Deep Residual Networks for Single Image Super-Resolution》论文阅读之EDSR
- 4. 论文阅读：Lightweight Pyramid Networks for Image Deraining
- 5. 论文阅读《Dynamic Image Networks for Action Recognition》
- 6. 论文阅读：Multi-Scale Progressive Fusion Network for Single Image Deraining
- 7. 论文阅读：Collaborative Deep Embedding Via Dual Networks
- 8. 论文阅读（【CVPR2018】Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision）
- 9. 【论文阅读】【CVPR2017】Dual Attention Networks for Multimodal Reasoning and Matching
- 10. 读论文：Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration