real-esrgan x2+

Real-ESRGAN x2 is an image upscaling algorithm that uses deep learning to enhance the resolution and quality of images. It is a state-of-the-art model that can upscale images by a factor of 2, meaning it can double the size of an image while preserving its quality and details. Real-ESRGAN x2 is based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) architecture, which uses a combination of convolutional neural networks and adversarial training to generate high-quality images. The "real" in its name refers to the fact that it aims to create realistic, natural-looking images, as opposed to overly processed or artificial ones.

相关问题

git clone https://github.com/xinntao/Real-ESRGAN.git cd Real-ESRGAN

在Git中，`git clone`命令是用来克隆远程仓库到本地的一个快捷方式。当你输入`git clone https://github.com/xinntao/Real-ESRGAN.git`时，这个命令会将GitHub上xinntao创建的名为"Real-ESRGAN"的项目复制一份到你的本地计算机上。这个仓库通常包含源代码及相关文档。

接着，`cd Real-ESRGAN`则是改变当前工作目录（working directory），让你进入刚刚克隆好的Real-ESRGAN项目文件夹，这样你就能够在该目录下直接进行后续的开发、查看代码等工作。

执行这两个命令后，你可以在终端中看到类似这样的提示：

$ git clone https://github.com/xinntao/Real-ESRGAN.git
Cloning into 'Real-ESRGAN'...
remote: Enumerating objects: XX, XX bytes (XX), XX already up to date.
$ cd Real-ESRGAN

这里的数字可能会因实际项目大小而变化。

Real-ESRGAN

Real-ESRGAN是一个用于图像超分辨率的算法和网络。它的目标是通过训练使用纯合成数据的网络来实现真实世界的盲目超分辨率。论文标题为《Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data》。这个算法是基于ESRGAN（Enhanced Super-Resolution Generative Adversarial Networks）超分辨率网络的改进版本。实际上，Real-ESRGAN的NCNN实现也可以在GitHub上找到。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [AI数字人：让图片变清晰的图像超分辨率模型 Real-ESRGAN](https://blog.csdn.net/lsb2002/article/details/131724463)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *2* [Real-ESRGAN超分辨网络](https://blog.csdn.net/weixin_46515047/article/details/119829454)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *3* [【论文阅读+测试】Real-Esrgan超分辨率算法](https://blog.csdn.net/qq_46258063/article/details/126266621)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
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