论文标题
TARSIER：超分辨率gan中不断发展的噪声
Tarsier: Evolving Noise Injection in Super-Resolution GANs
论文作者
论文摘要
超分辨率旨在提高图像中的分辨率和细节水平。 Nesrgan+持有一般单图像超级分辨率的当前最新技术状态，Nesrgan+在训练时每个残留层都注入了高斯噪声。在本文中，我们利用进化方法来改善Nesrgan+通过在推理时优化噪声注入。更确切地说，我们使用对角线CMA根据结合质量评估和现实主义的新标准来优化注入的噪声。我们的结果通过PIRM认知评分和人类研究来验证。我们的方法在几个标准的超级分辨率数据集上优于Nesrgan+。更一般而言，我们的方法可用于优化基于噪声注入的任何方法。
Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve NESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.
