讀論文：Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
源碼：[https://github.com/cszn/SRMD] 1 介紹 1.在典型的SISR框架中，將LR圖像y建模爲以下退化過程的輸出： x表示：HR圖像 k表示：模糊核 ↓s表示：縮小s倍的降採樣操作 n表示：加性高斯白噪聲 2.本文的主要貢獻有以下三點： （1）提出了一個簡單而有效且可擴展的CNN框架，超越了廣泛使用的雙三次降質假設，並且適用於多種甚至是空間變異的降級。 （2）提出了一
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相關文章
- 1. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations論文閱讀
- 2. SRMD:Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
- 3. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations 論文筆記
- 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. 論文閱讀《Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching》
- 7. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting論文閱讀
- 8. 【論文閱讀】Learning a Deep Convolutional Network for Image Super-Resolution
- 9. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 10. 【論文閱讀】Learning Deconvolution Network for Semantic Segmentation