title: Image Super-Resolution Using Deep Convolutional Networks.
abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) 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 methods 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. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
authors.forename: Chao
authors.initials: C
authors.lastname: Dong
authors.forename: Chen Change
authors.initials: CC
authors.lastname: Loy
authors.forename: Kaiming
authors.initials: K
authors.lastname: He
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Tang