2020CVPR《Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution》論文閱讀
個人對2020cvpr的超分網絡DRN的一些理解！ 論文鏈接：https://arxiv.org/pdf/2003.07018.pdf 源碼鏈接：https://github.com/fengye-lu/DRN-master 論文的出發點：**目前學習從LR到HR的映射關係的超分網絡存在兩個問題（1）超分是一個「病態」問題，一張LR圖像可以對應多個HR圖像，就是說其實LR圖像可以由多個HR圖像降採
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相關文章
- 1. DRN ---Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
- 2. 《Deep Adaptive Inference Networks for Single Image Super-Resolution》論文閱讀
- 3. 《Enhanced Deep Residual Networks for Single Image Super-Resolution》論文閱讀之EDSR
- 4. 論文閱讀：Lightweight Pyramid Networks for Image Deraining
- 5. 論文閱讀《Dynamic Image Networks for Action Recognition》
- 6. 論文閱讀：Multi-Scale Progressive Fusion Network for Single Image Deraining
- 7. 論文閱讀：Collaborative Deep Embedding Via Dual Networks
- 8. 論文閱讀（【CVPR2018】Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision）
- 9. 【論文閱讀】【CVPR2017】Dual Attention Networks for Multimodal Reasoning and Matching
- 10. 讀論文：Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration