Visual Media Super-Resolution Using Super-Resolution Generative Adversarial Networks

Deep learning has made significant advances in a variety of domains in recent years, including recognition of images, speech, and natural language processing and video super-resolution. Recovery of a high-resolution (HR) picture from a low-resolution (LR) counterpart is the aim of super-resolution (SR). It is an enduring and difficult part of image processing with numerous real-world applications including reconstruction of medical images, face recognition, HDTV, UAV surveillance, super-resolution panoramic video, and remote sensing. We aim to develop an application to provide a one-stop solution for converting both low-resolution images and videos to high-resolution images and videos. Our intention is to deliver the power of AI and deep learning to the general community by wrapping it in an API that can be seamlessly integrated with a Web application so that users can experience the benefits of the method of super-resolution using deep learning without reinventing the wheel.