Single Image Super-Resolution techniques have the function of retrieving a high resolution image from a single low resolution input. They implement deep learning heuristics which perform the techniques to form pixel-accourate reproductions. In this paper we have experimented upon various neural architectures with unique approaches towards the task of super-resolution. We have especially elaborated upon adversarial training networks which are yielding progressive results in both conditional and quantifiable benchmarks. © Springer Nature Singapore Pte Ltd. 2019.