We introduce SinGAN, an unconditional generative model that can be learned
from a single natural image. Our model is trained to capture the internal
distribution of patches within the image, and is then able to generate high
quality, diverse samples that carry the same visual content as the image.
SinGAN contains a pyramid of fully convolutional GANs, each responsible for
learning the patch distribution at a different scale of the image. This allows
generating new samples of arbitrary size and aspect ratio, that have
significant variability, yet maintain both the global structure and the fine
textures of the training image. In contrast to previous single image GAN
schemes, our approach is not limited to texture images, and is not conditional
(i.e. it generates samples from noise). User studies confirm that the generated
samples are commonly confused to be real images. We illustrate the utility of
SinGAN in a wide range of image manipulation tasks.

SinGAN 是一种无条件生成模型，可以从单个自然图像中进行学习，并且可以用于多种图像操作任务。其包含一系列基于全卷积神经网络的生成器，可以学习图像的不同尺度上的内部数据分布，并生成具有相同视觉内容的高质量、多样化的样本。用户研究表明，生成的样本往往被误认为是真实图像。

SinGAN：从单张自然图像学习生成模型

SinGAN: Learning a Generative Model from a Single Natural Image

In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.

本文提出了一种自然图像先验，直接代表了自然图像分布的高斯平滑版本，包括在基于 Bayes 估计量的图像恢复公式中，可用于解决噪声失真的图像恢复问题，利用去噪自编码器学习均值漂移向量场，再应用于 Bayes 风险最小化的梯度下降法，成功实现了媲美竞争者的噪声盲去模糊、超分辨率和去马赛克结果。