RealSR-Zero dataset provided by our paper "Criteria Comparative Learning for Real-scene Image Super-Resolution" [arxiv] [IEEE]
Link: Google Drive, Baidu Drive(ljdr)
RealSR-Zero consists of 45 LR images, which are shot by a iPhone4 device in different time, place and user. We collect them from internet, and the shooting period is 2011-2013 year. To modeling a challenge real-world scene, only poor-quality image are provided for evaluation. Thus, we adopt label-free quality assessment metric NIQE, to verity each method.
NIQE and MOS result for RealSR-Zero
| Methods | ESRGAN | Impressionism | DASR | Real-ESRGAN | Ours |
|---|---|---|---|---|---|
| NIQE | 6.066 | 4.961 | 5.838 | 4.575 | 4.409 |
| MOS | 4.275 | 3.455 | 4.470 | 3.245 | 3.050 |
Visualization
If you find this work useful for your research, please cite:
@artical{shi2022realsr,
title={Criteria Comparative Learning for Real-scene Image Super-Resolution},
author={Shi, Yukai and Li, Hao and Zhang, Sen and Yang, Zhijing and Wang, Xiao},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2022}
}
Please contact me if there is any question (Hao Li: [email protected]) (Yukai Shi: [email protected]).