Edge-Informed Single Image Super-Resolution
这篇文章提出了一种将图像结构信息和纹理信息分开处理的单图像超分辨率方法，通过这种方法将单图像超分辨率转换为图像修复。 该文章首先分析了传统的双三次插值的弊端： 原标准的HR图像是有边缘信息的高清图像，2倍下采样时四个像素合成一个像素点使下采样后的图片丢失了高频边缘的结构信息和方向信息，后双三次插值得到的2倍恢复的原图彻底丢失了高频边缘的结构信息和方向信息。 该文章提出了一种，即通过插值法将图像放大
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相关文章
- 1. 【超分辨率】Enhanced Deep Residual Networks for Single Image Super-Resolution
- 2. DeeperLab: Single-Shot Image Parser 详解
- 3. 3D Face Reconstruction from a Single Image
- 4. AIPNet: Image-to-Image Single Image Dehazing with Atmospheric Illumination Prior
- 5. GANimation: Anatomically-aware Facial Animation from a Single Image
- 6. RAM: Residual Attention Module for Single Image Super-Resolution
- 7. SinGAN：Learning a Generative Model from a Single Natural Image
- 8. Depth-attentional Features for Single-image Rain Removal
- 9. GANimation: Anatomically-aware Facial Animation from a Single Image（ECCV18）
- 10. A Cascaded Convolutional Neural Network for Single Image Dehazing