Abstract

We propose a deep learning method for single image super- resolution (SR). Our method directly learns an end-to-end mapping be- tween the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the low- resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional meth- ods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.