  Fluorescein Angiography (FA) is a technique that employs the designated
camera for Fundus photography incorporating excitation and barrier filters. FA
also requires fluorescein dye that is injected intravenously, which might cause
adverse effects ranging from nausea, vomiting to even fatal anaphylaxis.
Currently, no other fast and non-invasive technique exists that can generate FA
without coupling with Fundus photography. To eradicate the need for an invasive
FA extraction procedure, we introduce an Attention-based Generative network
that can synthesize Fluorescein Angiography from Fundus images. The proposed
gan incorporates multiple attention based skip connections in generators and
comprises novel residual blocks for both generators and discriminators. It
utilizes reconstruction, feature-matching, and perceptual loss along with
adversarial training to produces realistic Angiograms that is hard for experts
to distinguish from real ones. Our experiments confirm that the proposed
architecture surpasses recent state-of-the-art generative networks for
fundus-to-angio translation task.

Image and Video Processing;Computer Vision and Pattern Recognition