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.
Evaluated on RGB channels, scale pixels in each border are cropped before evaluation.
The metrics are PSNR / SSIM .
| Method | Set5 | Set14 | DIV2K | Download |
|---|---|---|---|---|
| srcnn_x4k915_1x16_1000k_div2k | 28.4316 / 0.8099 | 25.6486 / 0.7014 | 27.7460 / 0.7854 | model | log |
@article{dong2015image,
title={Image super-resolution using deep convolutional networks},
author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={38},
number={2},
pages={295--307},
year={2015},
publisher={IEEE}
}