Abstract
Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversarial training (GAN), that pushes the output images to lie on the manifold of natural images. GANs are very powerful, but not perfect. They are hard to train and the results still often suffer from artifacts. In this paper we propose a complementary approach, that could be applied with or without GAN, whose goal is to train a feed-forward CNN to maintain natural internal statistics. We look explicitly at the distribution of features in an image and train the network to generate images with natural feature distributions. Our approach reduces by orders of magnitude the number of images required for training and achieves state-of-the-art results on both single-image super-resolution, and high-resolution surface normal estimation.
Abstract (translated)
维护自然图像统计是恢复和生成逼真图像的关键因素。在训练CNN时，通常通过对抗训练（GAN）尝试照片写实，其将输出图像推到自然图像的多个部分上。 GAN非常强大，但并不完美。他们很难训练，结果仍然经常受到文物的影响。在本文中，我们提出了一种补充方法，可以在有或没有GAN的情况下应用，其目标是训练前馈CNN以维持自然的内部统计。我们明确地查看图像中的要素分布并训练网络以生成具有自然特征分布的图像。我们的方法将训练所需的图像数量减少了几个数量级，并在单图像超分辨率和高分辨率表面法线估计上实现了最先进的结果。
URL
https://arxiv.org/abs/1803.04626
