Accurate Image Super-Resolution Using Very Deep Convolutional Networks笔记
摘要： （1）网络结构深：a significant improvement in accuracy 通过级联多层小的卷积核，上下文信息被高效利用 （2）深层网络存在的问题是收敛速度 采用学习残差和高的学习率 1.介绍 srcnn相对于传统学习方法，不需要设计典型特征，但是有3个问题：本文改善方法 （1）依赖于小区域的上下文信息（个人理解是由于网络层少，视野比较小） 深层
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相关文章
- 1. Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 2. Accurate Image Super-Resolution Using Very Deep Convolutional Networks阅读笔记
- 3. 【论文笔记-VDSR】Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 4. 【超分辨率】VDSR--Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 5. 【论文阅读】Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 6. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 7. Very Deep Convolutional NetWorks for Large-Scale Image Recognition
- 8. 论文笔记：Very Deep Convolutional Networks for Large-Scale Image Recognition
- 9. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 10. VGG——Very deep convolutional networks for large-scale image recognition