SRMD:Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Kai Zhang, Wangmeng Zuo, Lei Zhang CVPR2018 摘要 之前的方法都是用bicubic下采樣的數據做LR，導致模型處理退化過程不符合bicubic的LR時效果差。另外一點就是前人的模型可擴展性差，
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相關文章
- 1. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations 論文筆記
- 2. 讀論文：Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
- 3. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations論文閱讀
- 4. 2019-02-26 論文閱讀：Learning a Single Convolutional Super-Resolution Network for Multiple Degradations..
- 5. Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
- 6. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 7. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
- 8. Tensorflow - Implement for a Convolutional Neural Network on MNIST.
- 9. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- 10. DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition