  Deep learning has become a prominent computational modeling tool in the areas
of computer vision and image processing in recent years. This research
comprehensively analyzes the different deep-learning methods used for
image-to-image translation and reconstruction in the natural and medical
imaging domains. We examine the famous deep learning frameworks, such as
convolutional neural networks and generative adversarial networks, and their
variants, delving into the fundamental principles and difficulties of each. In
the field of natural computer vision, we investigate the development and
extension of various deep-learning generative models. In comparison, we
investigate the possible applications of deep learning to generative medical
imaging problems, including medical image translation, MRI reconstruction, and
multi-contrast MRI synthesis. This thorough review provides scholars and
practitioners in the areas of generative computer vision and medical imaging
with useful insights for summarizing past works and getting insight into future
research paths.
