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

This paper introduces SRGAN, a deep learning model using Generative Adversarial Networks (GANs) to enhance the resolution of images. Unlike previous methods that focus on pixel-wise accuracy, SRGAN aims to create more realistic and visually appealing high-resolution images by incorporating a perceptual loss function and adversarial training. This approach results in images with finer details and textures, making them closer to real-world scenes.