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

This paper introduces a new approach to single image super-resolution (SR) using a deep convolutional neural network. The network is trained to generate high-resolution images from low-resolution inputs, focusing on recovering realistic textures. By using a generative adversarial network (GAN) framework, the model learns to produce more convincing and detailed images compared to traditional SR methods, effectively enhancing the visual quality and realism of the upscaled images.