Advanced Super-Resolution using Lossless Pooling Convolutional Networks
該文章主要利用了視頻超分中的思想,即利用視頻中時間閾上的多幀圖像(如t,t-1,t-2,t+1,t+2)比利用單幀圖像超分更有優勢,因爲多幀圖像中圖像上的特徵都產生了細微的變化,因此可以提取更多的特徵信息,從而可以互補的生成SR圖像,運用這個思想,本文提出了一個增強超分生成圖像的模型. 首先提出了一個無損池化的思想即一個逆亞像素卷積將一張圖上的象素點按照採樣因子的比例進行分割如圖上所示進行2倍採樣
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相關文章
- 1. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 2. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- 3. 【OverFeat】《OverFeat：Integrated Recognition, Localization and Detection using Convolutional Networks》
- 4. Chained Predictions Using Convolutional Neural Networks
- 5. Emotion Recognition Using Graph Convolutional Networks
- 6. Fast Face-swap Using Convolutional Neural Networks
- 7. SPPNet：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 8. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
- 9. 論文 | Predicting Mortgage Default Using Convolutional Neural Networks
- 10. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks