Optical Coherence Tomography Angiography (OCTA) is a powerful imaging technique that provides non-invasive visualization of vascular networks, primarily in the eye but also in the skin. By capturing multiple optical coherence tomography (OCT) B-scans at the same location over time, OCTA identifies motion contrast arising from flowing blood cells. In this thesis, we propose a novel approach to accelerate OCTA data acquisition by leveraging Generative Adversarial Networks (GANs). The primary objective is to generate high-quality OCTA enface images from lower-quality inputs, which are typically derived from only two OCT volumes, compared to the higher-quality images used as ground truth that require four OCT volumes.