Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

This paper introduces SRGAN, a deep learning model that uses generative adversarial networks (GANs) to enhance the resolution of images. Unlike previous methods that focused on pixel-wise accuracy, SRGAN aims to generate more realistic and visually appealing high-resolution images by training a generator network to fool a discriminator network. The generator creates upscaled images, while the discriminator tries to distinguish between real high-resolution images and those generated by the generator. This approach results in images with finer details and improved perceptual quality.