ECCV2018超分辨率RCAN：Image Super-Resolution Using Very Deep Residual Channel Attention Networks
總的來說，這篇論文的主要做了兩件事，第一，設計RIR，加深網絡；第二，引入通道注意力機制（CA），說白了就是給通道加一個權重，表明不同通道提取出來的不同特徵的重要性不同，這個權重是學習出來的，後文有詳細介紹。 提出問題 （1）更深層次的圖像SR網絡更難訓練，僅僅通過疊加剩餘塊來構建更深層次的網絡很難得到更好的改善。 （2）低分辨率的輸入和特徵包含豐富的低頻信息，而目前主流的基於cnn的方法對每個信
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相關文章
- 1. 《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》
- 2. RCAN Image Super-Resolution Using Very Deep Residual Channel Attention Networks-ECCV2018
- 3. SR文獻學習之《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》
- 4. Image Super-Resolution Using Very Deep Residual Channel Attention Networks 翻譯
- 5. 【超分辨率】VDSR--Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 6. 論文閱讀筆記之——《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》RCAN
- 7. 【超分辨率】Enhanced Deep Residual Networks for Single Image Super-Resolution
- 8. 降噪、超分辨率RED-Net之Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetr
- 9. Channel Pruning for Accelerating Very Deep Neural Networks
- 10. Accurate Image Super-Resolution Using Very Deep Convolutional Networks