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Maintaining natural image statistics is a crucial factor in restoration andgeneration of realistic looking images. When training CNNs, photorealism isusually attempted by adversarial training (GAN), that pushes the output imagesto lie on the manifold of natural images. GANs are very powerful, but notperfect. They are hard to train and the results still often suffer fromartifacts. In this paper we propose a complementary approach, that could beapplied with or without GAN, whose goal is to train a feed-forward CNN tomaintain natural internal statistics. We look explicitly at the distribution offeatures in an image and train the network to generate images with naturalfeature distributions. Our approach reduces by orders of magnitude the numberof images required for training and achieves state-of-the-art results on bothsingle-image super-resolution, and high-resolution surface normal estimation.

Maintaining Natural Image Statistics with the Contextual Loss