Image Super-Resolution Using Deep Convolutional Networks

This paper introduces a deep convolutional neural network for single image super-resolution (SR). It learns the mapping between low-resolution and high-resolution images directly from data, avoiding hand-engineered features. The network demonstrates superior performance in terms of accuracy and visual quality compared to traditional SR methods.