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We propose an image super resolution(ISR) method using generative adversarialnetworks (GANs) that takes a low resolution input fundus image and generates ahigh resolution super resolved (SR) image upto scaling factor of $16$. Thisfacilitates more accurate automated image analysis, especially for small orblurred landmarks and pathologies. Local saliency maps, which define eachpixel's importance, are used to define a novel saliency loss in the GAN costfunction. Experimental results show the resulting SR images have perceptualquality very close to the original images and perform better than competingmethods that do not weigh pixels according to their importance. When used forretinal vasculature segmentation, our SR images result in accuracy levels closeto those obtained when using the original images.

Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution