  We propose an image super resolution(ISR) method using generative adversarial
networks (GANs) that takes a low resolution input fundus image and generates a
high resolution super resolved (SR) image upto scaling factor of $16$. This
facilitates more accurate automated image analysis, especially for small or
blurred landmarks and pathologies. Local saliency maps, which define each
pixel's importance, are used to define a novel saliency loss in the GAN cost
function. Experimental results show the resulting SR images have perceptual
quality very close to the original images and perform better than competing
methods that do not weigh pixels according to their importance. When used for
retinal vasculature segmentation, our SR images result in accuracy levels close
to those obtained when using the original images.
