8 papers with code • 1 benchmarks • 0 datasets
Generating Retinal Fluorescein Angiography from Retinal Fundus Image using Generative Adversarial Networks.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
Ranked #1 on Image-to-Image Translation on Aerial-to-Map
We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner.
Ranked #1 on Image-to-Image Translation on cat2dog (Kernel Inception Distance metric)
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
Ranked #1 on Image-to-Image Translation on AFHQ
We also prove that the 3D VHIP with Fixed CoP is the same as its 2D version, and we generalize controllers working on the 2D VHIP to the 3D VHIP.
Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters.
The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA).
Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal.