CVPR2020-對偶迴歸與SISR | Closed-loop Matters：Dual Regression Networks for Single Image Super-Resolution
思想很新，效果也很好，CVPR的文章的確質量挺高。可以拿來作爲改進的baseline。 Github : https://github.com/guoyongcs/DRN. Abstract： 通過學習從低分辨率(LR)圖像到高分辨率(HR)圖像的非線性映射函數，深度神經網絡在圖像超分辨率(SR)方面表現出了良好的性能。然而，現有的SR方法有兩個潛在的限制。首先，學習從LR到HR圖像的映射函數是一
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相關文章
- 1. 2020CVPR《Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution》論文閱讀
- 2. DRN ---Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
- 3. Enhanced Deep Residual Networks for Single Image Super-Resolution（EDSR）
- 4. DRN（CVPR2020）——SISR超分辨
- 5. 《Deep Adaptive Inference Networks for Single Image Super-Resolution》論文閱讀
- 6. 《Deep Adaptive Inference Networks for Single Image Super-Resolution》閱讀筆記
- 7. Enhanced Deep Residual Networks for Single Image Super-Resolution
- 8. SR-Enhanced Deep Residual Networks for Single Image Super-Resolution
- 9. 【超分辨率】Enhanced Deep Residual Networks for Single Image Super-Resolution
- 10. AdaDSR（Deep Adaptive Inference Networks for Single Image Super-Resolution論文筆記）