Learning a Deep Convolutional Network for Image Super-Resolution
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) [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 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.
Cite
Text
Dong et al. "Learning a Deep Convolutional Network for Image Super-Resolution." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10593-2_13Markdown
[Dong et al. "Learning a Deep Convolutional Network for Image Super-Resolution." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/dong2014eccv-learning/) doi:10.1007/978-3-319-10593-2_13BibTeX
@inproceedings{dong2014eccv-learning,
title = {{Learning a Deep Convolutional Network for Image Super-Resolution}},
author = {Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {184-199},
doi = {10.1007/978-3-319-10593-2_13},
url = {https://mlanthology.org/eccv/2014/dong2014eccv-learning/}
}