Triple Attention Mixed Link Network for Single Image Super Resolution
該文章主要內容爲通過添加三重注意力機制來增強超分效果，實則就是提出了一個三重注意力機制的炒粉模型，首先從底層分析，三重注意力機制：①爲一個傳統的通道注意力機制，不過在此將全連接層變爲卷積層實施升維和降維代碼如下： ②提出了一個核注意力機制並列爲該論文的創新點：主要思路是將不同卷積核提取出的不同感受野下的特徵圖提取出來進行融合（即逐元素相加）後通過一個通道注意力機制形成一個參數，注意這裏爲一個參數圖
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相關文章
- 1. Second-order Attention Network for Single Image Super-Resolution（CVPR2019）
- 2. 【Super Resolution】【論文閱讀】【CVPR2019】Second-order Attention Network for Single Image Super-Resolution
- 3. RAM: Residual Attention Module for Single Image Super-Resolution
- 4. An Attention-Based Approach for Single Image Super Resolution
- 5. 【CVPR19 超分辨率】（Oral）Second-order Attention Network for Single Image Super-Resolution
- 6. Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based
- 7. Residual Dense Network for Image Super-Resolution
- 8. Multi-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution
- 9. CSAR——Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution
- 10. SR-Enhanced Deep Residual Networks for Single Image Super-Resolution