  Fundus images are very useful in identifying various ophthalmic disorders.
However, due to the presence of artifacts, the visibility of the retina is
severely affected. This may result in misdiagnosis of the disorder which may
lead to more complicated problems. Since deep learning is a powerful tool to
extract patterns from data without much human intervention, they can be applied
to image-to-image translation problems. An attempt has been made in this paper
to automatically rectify such artifacts present in the images of the fundus. We
use a CycleGAN based model which consists of residual blocks to reduce the
artifacts in the images. Significant improvements are seen when compared to the
existing techniques.
