论文标题
Esrgan+：进一步改善增强的超分辨率生成对抗网络
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
论文作者
论文摘要
增强的超分辨率生成对抗网络（ESRGAN）是一种感知驱动的单图像超级分辨率的方法，能够产生逼真的图像。尽管这些生成的图像具有视觉质量，但仍有改进的空间。以这种方式，该模型被扩展为进一步提高图像的感知质量。我们设计了一个新颖的块来代替原始的Esrgan使用的块。此外，我们将噪声输入引入发电机网络以利用随机变化。结果图像呈现出更现实的纹理。该代码可在https://github.com/ncarraz/esrganplus上找到。
Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is extended to further improve the perceptual quality of the images. We have designed a novel block to replace the one used by the original ESRGAN. Moreover, we introduce noise inputs to the generator network in order to exploit stochastic variation. The resulting images present more realistic textures. The code is available at https://github.com/ncarraz/ESRGANplus .
