Radio surveys are widely used to study active galactic nuclei. Radio interferometric Observations typically trade-off surface brightness sensitivity for angular resolution. Hence, observations using a wide range of baseline lengths are required to recover both bright small-scale structures and diffuse extended emission. We investigate if generative adversarial networks (GANs) can extract additional information from radio data and might ultimately recover extended flux from a survey with a high angular resolution and vice versa. We use a GAN for the image -to -image translation between two different data sets, namely the Faint Images of the Radio Sky at Twenty -Centimeters (FIRST) and the NRAO VIA Sky Survey (NVSS) radio surveys. The GAN is trained to generate the corresponding image cut-out from the other survey for a given input. The results are analysed with a variety of metrics, including structural similarity as well as flux and size comparison of the extracted sources. RadioGAN is able to recover extended flux density within a 20 per cent margin for almost half of the sources and learns more complex relations between sources in the two surveys than simply convolving them with a different synthesized beam. RadioGAN is also able to achieve subbeam resolution by recognizing complicated underlying structures from unresolved sources. RadioGAN generates over a third of the sources within a 20 per cent deviation from both original size and flux for the FIRST to NVSS translation, while for the NVSS to FIRST mapping it achieves almost 30 per cent within this range.