Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
写在前面的话 好久没有看论文了。今天看到的这篇论文，感觉motivation相当不错，从论文中的表格来看，论文的PSNR和SSIM值又被刷上了新的高度，而且使用的参数竟然只有接近6M，相比较于之前的RCAN和SAN，这篇论文只使用了接近1/3的参数。但是有一个疑问就是：论文确实展示了实验结果表格， 但是在图片的展示环节中却并没有和这些方法进行比较，后面将会对这篇论文的实验代码进行复现，请时刻关注我
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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