Component Divide-and-Conquer for Real-World Image Super-Resolution(CDC)

This repository is an official PyTorch implementation of the paper "Component Divide-and-Conquer for Real-World Image Super-Resolution " from ECCV 2020. [PDF]

We provide full training and testing codes, pre-trained models and the large-scale dataset used in our paper. You can train your model from scratch, or use a pre-trained model to enlarge your images.

Code

Dependencies

• Python 3.6
• PyTorch >= 1.1.0
• numpy
• cv2
• skimage
• tqdm

Quick Start

Clone this github repo.

git clone https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution
cd Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution/CDC

Training

1. Download our dataset and unpack them to any place you want. Then, change the dataroot and test_dataroot argument in ./options/realSR_HGSR_MSHR.py to the place where images are located.
2. Run CDC_train_test.py using script file train_pc.sh.

sh ./train_pc.sh cdc_x4 ./CDC_train_test.py ./options/realSR_HGSR_MSHR.py 1

3. You can find the results in ./experiments/CDC-X4 if the exp_name argument in ./options/realSR_HGSR_MSHR.py is CDC-X4

Testing

1. Download our pre-trained models to ./models folder or use your pre-trained models
2. Change the test_dataroot argument in CDC_test.py to the place where images are located
3. Run CDC_test.py using script file test_models_pc.sh.

sh test_models_pc.sh cdc_x4_test ./CDC_test.py ./models/HGSR-MHR_X4_SubRegion_GW_283.pth 1

4. You can find the enlarged images in ./results folder

Pretrained models

1. 2X Models
2. 3X Models
3. 4X Models

The above provided models are both trained on our dataset with our gradient-weighted loss.

Dataset

Please download our dataset from Google Drive or Baidu Drive. The verification code is osiy. There are 31970 192×192 patches cropped for training and 93 image pairs for testing.

Methods	Scale	PSNR	SSIM	LPIPS
Bicubic	2	32.67	0.887	0.201
EDSR	2	34.24	0.908	0.155
RCAN	2	34.34	0.908	0.158
CDC(ours)	2	34.45	0.910	0.146
Bicubic	3	31.50	0.835	0.362
EDSR	3	32.93	0.876	0.241
RCAN	3	33.03	0.876	0.241
CDC(ours)	3	33.06	0.876	0.244
Bicubic	4	30.56	0.820	0.438
EDSR	4	32.03	0.855	0.307
RCAN	4	31.85	0.857	0.305
CDC(ours)	4	32.42	0.861	0.300

Citation

If you find our work useful in your research or publication, please cite:

@InProceedings{wei2020cdc,
  author = {Pengxu Wei, Ziwei Xie, Hannan Lu, ZongYuan Zhan, Qixiang Ye, Wangmeng Zuo, Liang Lin},
  title = {Component Divide-and-Conquer for Real-World Image Super-Resolution},
  booktitle = {Proceedings of the European Conference on Computer Vision},
  year = {2020}
}