Image Super-Resolution Using Deep Convolutional Networks

This paper introduces a deep convolutional neural network (CNN) approach for single image super-resolution (SR). It learns the mapping directly from low-resolution to high-resolution images, avoiding hand-engineered features. The network is trained end-to-end and shows excellent performance in recovering fine details from images, surpassing many existing SR methods.