Image Super-Resolution with Adversarial Learning

Single-image super-resolution refers to the problem of generating a high-resolution image from a low-resolution one. In this work we address to the problem of single-image super-resolution of degraded low-resolution images. We face this task by adopting Convolutional Neural Networks, in particular we combine unsupervised adversarial learning with techniques aimed to preserve the color information and the spatial smoothness on the produced images.