We show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR). While most existing SR approaches attempt to generate realistic textures through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN. But unlike prevalent GAN inversion methods that require expensive image-specific optimization at runtime, our approach only needs a single forward pass to generate the upscaled image. GLEAN can be easily incorporated in a simple encoder-bank-decoder architecture with multi-resolution skip connections. Switching the bank allows the method to deal with images from diverse categories, e.g., cat, building, human face, and car. Images upscaled by GLEAN show clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods.

翻译：我们显示,经过培训的基因反转网络(GANs),例如StyleGAN(StyleGAN),可以用作提高大型因素图像超分辨率(SR)恢复质量的潜在银行。 虽然大多数现有的SR方法试图通过学习对抗性损失产生现实的纹理,但我们的方法,即General LatEnt bANk(GLEAN),通过直接利用预先培训的GAN所包装的丰富多样的前科,超越了现有的做法。 但是,与普遍流行的GAN反转方法不同,在运行时需要昂贵的图像优化,我们的方法只需要一个前方传球来生成升级图像。 GLEAN可以很容易地纳入一个简单的编码器-银行解码器结构,而多分辨率连接会跳过。 转换银行使处理不同类别的图像的方法(例如猫、建筑、人脸和汽车)的方法得以处理。 GLANEAN所提升的图像在与现有方法相比在忠诚和文字忠实性方面显示出明显的改进。