Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
本文提出了一个低频率纹理在高复杂度网络训练下会过拟合导致效果变差的问题，而现行网络大多都忽视了这个问题将低频纹理与高频纹理一起输入到深层复杂的网络中 为了解决这个问题这篇论文对纹理进行了分层处理总体框架分为三个部分分别为浅层特征提取、分层处理不同频率的特征、最后重构生成HR图片 其中这篇论文最主要的就是如何将高频与低频信息分开处理，首先将浅特征提取曾得到的特征图输入第一个BRM块中经过反卷积或亚像
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相关文章
- 1. Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
- 2. A Recursive Restoration Model for Single-Image Super-Resolution(EBRN ICCV2019)
- 3. [图像超分]--ICCV2019-Embedded Block Residual Network
- 4. 【图像复原】RDN论文详解（Residual Dense Network for Image Restoration）
- 5. 论文解读之MemNet: A Persistent Memory Network for Image Restoration
- 6. 《Dense Residual Network for Retinal Vessel Segmentation》
- 7. Paper | Dynamic Residual Dense Network for Image Denoising
- 8. 人群密度估计--Learning a perspective-embedded deconvolution network for crowd counting
- 9. Residual attention network for image classification
- 10. A Deep One-class Model for Network Anomaly Detection