Single Image Super-Resolution (SISR) is looking at restoring the missing high-resolution information from a single low-resolution image in order to increase the apparent spatial resolution by a factor of two or more. In recent years, convolution neural networks have been applied with great success to the problem of improving spatial resolution from a single image. With the advent of low-resolution (10 m) optical sensors such as Sentinel-2, it is interesting to explore the possibility of improving image resolution with Deep Learning (DL) techniques. The purpose of this article is to investigate the potential performances of recent DL super-resolution techniques. The techniques explored here include not only techniques for enhancing high-frequency content but also so-called image-to-image translation techniques based on Generative Adversarial Neural Networks (GAN). From our preliminary results, we show that GANs have the ability to restore complex textural information.