https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw
Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)
https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw
deep-learning epsr pirm-sr pytorch super-resolution
Last synced: 4 months ago
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Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)
- Host: GitHub
- URL: https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw
- Owner: subeeshvasu
- License: mit
- Created: 2018-09-14T10:09:52.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-07-14T08:12:06.000Z (over 5 years ago)
- Last Synced: 2025-04-10T20:39:36.672Z (7 months ago)
- Topics: deep-learning, epsr, pirm-sr, pytorch, super-resolution
- Language: Python
- Homepage:
- Size: 3.99 MB
- Stars: 80
- Watchers: 4
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - EPSR - Distortion Tradeoff using Enhanced Perceptual Super-resolution Network](https://arxiv.org/pdf/1811.00344.pdf). This work has won the first place in PIRM2018-SR competition (region 1) held as part of the ECCV 2018. (Paper implementations｜论文实现 / Other libraries｜其他库:)
- Awesome-pytorch-list - EPSR - Distortion Tradeoff using Enhanced Perceptual Super-resolution Network](https://arxiv.org/pdf/1811.00344.pdf). This work has won the first place in PIRM2018-SR competition (region 1) held as part of the ECCV 2018. (Paper implementations / Other libraries:)
README
# EPSR (Enhanced Perceptual Super-resolution Network) [paper](https://drive.google.com/file/d/102SPK0bhklUWFYhOEIJSwbw0m614Fjqa/view)
This repo provides the test code, pretrained models, and results on benchmark datasets of our work. We (IPCV_team) won the first place in [PIRM2018-SR competition](https://www.pirm2018.org/PIRM-SR.html) (region 1). We were also ranked as second and thrid in region 2 and 3 respectively. For details refer to our recently accepted paper in [ECCV2018 PIRM Workshop](https://pirm2018.org/).
"Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network", [Subeesh Vasu](https://subeeshvasu.github.io), [Nimisha T. M.](https://nimiiit.github.io/) and [A. N. Rajagopalan](http://www.ee.iitm.ac.in/~raju/), Perceptual Image Restoration and Manipulation (PIRM) Workshop and Challenge, Eurpean Conference on Computer Vision Workshops (ECCVW 2018), Munich, Germany, September 2018. [[arXiv]](https://arxiv.org/pdf/1811.00344.pdf)
#### BibTeX
@inproceedings{vasu2018analyzing,
author = {Vasu, Subeesh and T.M., Nimisha and Rajagopalan, A.N.},
title = {Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network},
booktitle = {European Conference on Computer Vision (ECCV) Workshops},
year = {2018}}
## Results
Visual comparison for 4× SR with bicubic interpolation model on PIRM-self, BSD100, and Urban100 datasets. Here IHR refers to the ground truth HR image. [SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html), [EDSR](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf), [DBPN](http://openaccess.thecvf.com/content_cvpr_2018/papers/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.pdf), [ENet](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf), and [CX](https://arxiv.org/pdf/1803.04626.pdf) are existing works. EPSR1, EPSR2, and EPSR3 are the results of our approach (EPSR) corresponding to region 1, 2, and 3 of PIRM-SR challenge. BNet1, BNet2, and BNet3 are the results of our baseline network.
Perception-distortion trade-off between BNet and EPSR. For both methods, the above plot has the values corresponding to 19 model weights which span different regions on the perception-distortion plane and the corresponding curves that best fit these values.
Performance comparison of top 9 methods from PIRM-SR challenge. Methods are ranked based on the PI and RMSE values corresponding to the test data of PIRM-SR. The entries from our approach are highlighted in red. Methods with a marginal difference in PI and RMSE values share the same rank and are indicated with a " * ".
## Test
The code is built on the official implementation of [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch) and tested on Ubuntu 16.04 environment (Python3.6, PyTorch_0.4.0, CUDA8.0) with Titan X GPU. Refer [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch) for other dependencies. Test code of EPSR can be found in [EPSR_testcode](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw/tree/master/EPSR_testcode).
## Results on public benchmark datasets
- [PIRM-self](https://drive.google.com/file/d/1ottkNHZpSYBk9gMrc1T_iCHdMsQsIKAy/view?usp=sharing)
- [PIRM-test](https://drive.google.com/file/d/1OngQfvbpVXCFDHNZhZMAMGjTSVNAfkOb/view?usp=sharing)
- [BSD100](https://drive.google.com/file/d/12ABqYLYcIhCuYJkMs4HANarjj-OxzWQ2/view?usp=sharing)
- [Urban100](https://drive.google.com/file/d/1vgjberya6rYcYq7sTW-DsVOnadqvVYNM/view?usp=sharing)
- [Set14](https://drive.google.com/file/d/1FxlVy93o8ZbrCKtqYo8hkk5F4Vx9vRA9/view?usp=sharing)
- [Set5](https://drive.google.com/file/d/1I08xKTumupde5BNTEN_e7kJZTLbQeYnJ/view?usp=sharing)
## References
[SRCNN] Dong, C., Loy, C.C., He, K., Tang, X.: [Learning a deep convolutional network for image super-resolution](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html). ECCV 2014
[EDSR] Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: [Enhanced deep residual networks for single image super-resolution](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf). CVPR workshops 2017
[DBPN] Haris, M., Shakhnarovich, G., Ukita, N.: [Deep backprojection networks for super-resolution](http://openaccess.thecvf.com/content_cvpr_2018/papers/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.pdf). CVPR 2018
[ENet] Sajjadi, M.S., Sch ̈olkopf, B., Hirsch, M.: [Enhancenet: Single image super-resolution through automated texture synthesis](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf). ICCV 2017
[CX] Mechrez, R., Talmi, I., Shama, F., Zelnik-Manor, L. [Learning to maintain natural image statistics](https://arxiv.org/pdf/1803.04626.pdf). arXiv preprint arXiv:1803.04626 (2018)
[PIRM-SR challenge] Blau, Y., Mechrez, R., Timofte, R. [2018 PIRM Challenge on Perceptual Image Super-resolution](https://arxiv.org/pdf/1809.07517.pdf). arXiv preprint arXiv:1809.07517 (2018)
## Acknowledgements
This code is built on [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch). We thank the authors for sharing their codes of EDSR [PyTorch version](https://github.com/thstkdgus35/EDSR-PyTorch).