Fast and Accurate Single Image Super-Resolution via Information Distillation Network
本文主旨爲了再生成深度網絡的同時降低計算複雜性和減少內存消耗，因此本文提出了一個信息蒸餾網絡，降低複雜性和內存消耗。 總體網絡框架分爲三個部分①特徵提取模塊②增強模塊（蒸餾模塊）③特徵融合與生成圖片模塊 總體來說首先低分辨率圖片輸入到網絡中經過雙三次插值放大到指定大小輸入後層形成殘差網絡，後低分辨率圖片首先經過FBLOCK提取淺層特徵，後經過DBLOCK經過信息蒸餾再提取深層特徵的同時減少內存消耗
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相關文章
- 1. PaperNote-Fast and Accurate Single Image Super-Resolution via Information Distillation Network
- 2. IDN-Fast and Accurate Single Image Super-Resolution via Information Distillation Network信息蒸餾網絡
- 3. 【論文筆記2_超分辨】（IDN）Fast and Accurate Single Image Super-Resolution via Information Distillation Network
- 4. 文獻閱讀:Fast and Accurate Single Image Super-Resolution via Information Distillation N (IDN CVPR2018)
- 5. Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based
- 6. 文獻閱讀:Lightweight Image Super-Resolution with Information Multi-distillation Network (ACMM2019)
- 7. 論文閱讀：Fast Single Image Rain Removal via a Deep Decomposition-Composition Network
- 8. Single Image Dehazing via Conditional Generative Adversarial Network
- 9. Semantic Flow for Fast and Accurate Scene Parsing
- 10. PEPSI++: Fast and Lightweight Network for Image Inpainting | 簡記