Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution
這篇論文提出了現有的SISR大多未能充分利用中間層的特徵信息的問題，由此提出了一個特徵跳轉融合塊，來解決這個問題。 網絡總體結構由四部分組成：淺特徵提取層、深層融合特徵提取層、重建層、深度特徵提取層。輸入的LR圖片先經過33的卷積提取粗糙的特徵，後經過淺特徵提取層提取淺層特徵，淺層特徵經過深層融合特徵提取層的到經過融合後的深層特徵，各個深層特徵融合提取塊所得到的深層融合特徵經過全局特徵融合後，生成
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相關文章
- 1. 論文閱讀：FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
- 2. FSSD: Feature Fusion Single Shot Multibox Detector
- 3. 論文閱讀：Multi-Scale Progressive Fusion Network for Single Image Deraining
- 4. RefineDet + Dynamic Anchor Feature Selection for Single-Shot Object Detection
- 5. 論文閱讀：Multi-scale Optimal Fusion model for single image dehazing
- 6. [去霧論文]--AAAI2020-FFA-Net：Feature Fusion Attention Network for Single Image Dehazing
- 7. 論文閱讀：A Deep Tree-Structured Fusion Model for Single Image Deraining
- 8. Hyperspectral Image Classification With Deep Feature Fusion Network
- 9. SRFeat: Single Image Super-Resolution with Feature Discrimination
- 10. CSAR——Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution