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

This paper introduces SRGAN, a deep learning model that uses a Generative Adversarial Network (GAN) to enhance the resolution of images. It combines a convolutional neural network with an adversarial network to create more realistic and detailed high-resolution images from low-resolution inputs. The method focuses on recovering finer texture details, resulting in images that are perceptually more convincing to human observers.