读论文：Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
源码：[https://github.com/cszn/SRMD] 1 介绍 1.在典型的SISR框架中，将LR图像y建模为以下退化过程的输出： x表示：HR图像 k表示：模糊核 ↓s表示：缩小s倍的降采样操作 n表示：加性高斯白噪声 2.本文的主要贡献有以下三点： （1）提出了一个简单而有效且可扩展的CNN框架，超越了广泛使用的双三次降质假设，并且适用于多种甚至是空间变异的降级。 （2）提出了一
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相关文章
- 1. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations论文阅读
- 2. SRMD:Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
- 3. Learning a Single Convolutional Super-Resolution Network for Multiple Degradations 论文笔记
- 4. 2019-02-26 论文阅读：Learning a Single Convolutional Super-Resolution Network for Multiple Degradations..
- 5. Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
- 6. 论文阅读《Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching》
- 7. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting论文阅读
- 8. 【论文阅读】Learning a Deep Convolutional Network for Image Super-Resolution
- 9. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 10. 【论文阅读】Learning Deconvolution Network for Semantic Segmentation