https://github.com/shi-labs/ultrasr-arbitrary-scale-super-resolution
[Preprint] UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution, 2021
https://github.com/shi-labs/ultrasr-arbitrary-scale-super-resolution
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[Preprint] UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution, 2021
- Host: GitHub
- URL: https://github.com/shi-labs/ultrasr-arbitrary-scale-super-resolution
- Owner: SHI-Labs
- Created: 2021-03-20T02:23:58.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-24T19:55:12.000Z (over 4 years ago)
- Last Synced: 2025-05-29T09:07:45.521Z (4 months ago)
- Homepage: https://arxiv.org/abs/2103.12716
- Size: 1.47 MB
- Stars: 55
- Watchers: 12
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution
Paper Link: [ArXiv Preprint](https://arxiv.org/abs/2103.12716)
By [Xingqian Xu](https://ifp-uiuc.github.io/), [Zhangyang Wang](https://vita-group.github.io/), and [Humphrey Shi](https://www.humphreyshi.com),
[](https://www.youtube.com/watch?v=9ylD8TlSlyM)
## Introduction
*Arbitrary-scale super-resolution* is a raising research topic with tremendous application potentials. Prior CNN-based SR approaches usually apply to only one fixed resolution scale, and thus unable to adjust their output dimension without changing the low-resolution input. Such design creates a huge gap between academic research and practical usage, and a majority of image up-sampling applications, even sensitive to precision, still heavily relied on bicubic interpolation despite its poor quality. Empowered by the rapidly advancing techniques in implicit neural representation, images and scenes can now be generalized by network-learned implicit functions on various vision topics. Specifically for our SR task, the idea that use one trained network for all zoom-in scales on any input image will bring both convenience and accuracy to downstream users in the near future. In this work, we propose **UltraSR**, a simple yet effective new network design based on implicit image functions in which spatial coordinates and periodic encoding are deeply integrated with the implicit neural representation. Our UltraSR sets new state-of-the-art performance on the DIV2K benchmark on Arbitrary-scale super-resolution. UltraSR also achieves superior performance on other standard benchmark datasets in which it outperforms prior works in almost all experiments.
## Visualization
## Result
Result comparison on DIV2K validation dataset.
Method
x2
x3
x4
x6
x12
x18
x24
x30
Bicubic
31.01
28.22
26.66
24.82
22.27
21.00
20.19
19.59
EDSR
34.55
30.90
28.92
-
-
-
-
-
MetaSR-EDSR
34.64
30.93
28.92
26.61
23.55
22.03
21.06
20.37
LIIF-EDSR
34.67
30.96
29.00
26.75
23.71
22.17
21.18
20.48
UltraSR-EDSR
34.69
31.02
29.05
26.81
23.75
22.21
21.21
20.51
MetaSR-RDN
35.00
31.27
29.25
26.88
23.73
22.18
21.17
20.47
LIIF-RDN
34.99
31.26
29.27
26.99
23.89
22.34
21.31
20.59
UltraSR-RDN
35.00
31.30
29.32
27.03
23.93
22.36
21.33
20.61
Result comparison on other 5 benchmark datasets.
Dataset
Method
x2
x3
x4
x6
x8
x12
Set5
RDN
38.24
34.71
32.47
-
-
-
MetaSR-RDN
38.22
34.63
32.38
29.04
29.96
-
LIIF-RDN
38.17
34.68
32.50
29.15
27.14
24.86
UltraSR-RDN
38.21
34.67
32.49
29.33
27.24
24.81
Set14
RDN
34.01
30.57
28.81
-
-
-
MetaSR-RDN
33.98
30.54
28.78
26.51
24.97
-
LIIF-RDN
33.97
30.53
28.80
26.64
25.15
23.24
UltraSR-RDN
33.97
30.59
28.86
26.69
25.25
23.32
B100
RDN
32.34
29.26
27.72
-
-
-
MetaSR-RDN
32.33
29.26
27.71
25.90
24.83
-
LIIF-RDN
32.32
29.26
27.74
25.98
24.91
23.57
UltraSR-RDN
32.35
29.29
27.77
26.01
24.96
23.59
Urban100
RDN
32.89
28.80
26.61
-
-
-
MetaSR-RDN
32.92
28.82
26.55
23.99
22.59
-
LIIF-RDN
32.87
28.82
26.68
24.20
22.79
21.15
UltraSR-RDN
32.97
28.92
26.78
24.30
22.87
21.20
Manga109
RDN
39.18
34.13
31.00
-
-
-
MetaSR-RDN
-
-
-
-
-
-
LIIF-RDN
39.26
34.21
31.20
27.33
25.04
22.36
UltraSR-RDN
39.09
34.28
31.32
27.42
25.12
22.42
## License
## Citation
```bibtex
@article{xu2021ultrasr,
title={UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution},
author={Xingqian Xu and Zhangyang Wang and Humphrey Shi},
journal={arXiv preprint arXiv:2103.12716},
year={2021}
}
```
## Acknowledgements
Our work benefited greatly from [LIIF](https://github.com/yinboc/liif) (CVPR 2021) etc. We thank the authors for sharing their codes.