This repository is an official PyTorch implementation of the paper "Image Formation Model Guided Deep Image Super-Resolution" from AAAI 2020.
The code is built on EDSR (PyTorch) and tested on Ubuntu 16.04 environment (Python3.6, PyTorch_0.4.1, CUDA8.0, cuDNN5.1) with Tesla V100/1080Ti GPUs.

• ubuntu16.04
• Python 3.6(Recommend to use Anaconda)
• PyTorch0.4.1
• numpy
• skimage
• imageio
• matplotlib
• tqdm
• cv2

We use the DIV2K dataset to train our models. You can download it from here

You can evaluate our models with widely-used benchmark datasets:

Set5 - Bevilacqua et al. BMVC 2012,

Set14 - Zeyde et al. LNCS 2010,

B100 - Martin et al. ICCV 2001,

Urban100 - Huang et al. CVPR 2015.

All the models(X2, X3, X4) can be downloaded from GoogleDrive.

You can test our super-resolution algorithm with benchmarks. Please organize the testset in testset folder like this:

|--testset  
    |--Set5  
        |--LR
            |--X2
                |--babyx2.png  
                     ：   
                     ： 
            |--X3
            |--X4
        |--HR
            |--baby.png  
                 ：   
                 ： 

Then, run the following commands:

cd code
python main.py --dir_data ../testset --data_test Set5 --model physics_sr --pre_train ../models/X2/model_best.pt --scale 2 --save physics_sr_x2 --save_results --test_only

And generated results can be found in ./experiment/physics_sr_x2/results/

• To test all scales, you can modify the options(pre_train, scale) of the command above.
• To test other benchmarks, you can modify the option(data_test) of the command above.
• To change the save root, you can modify the option(save) of the command above.

If you have downloaded the trainset, please make sure that the trainset has been organized as follows:

|--DIV2K
    |--train  
        |--DIV2K_train_LR_bicubic
            |--X2
                |--0001x2.png  
                     ：   
                     ： 
            |--X3
            |--X4
        |--DIV2K_train_HR
            |--0001.png  
                 ：   
                 ： 

The command for training is as follow:

cd code
python main.py --dir_data <your root> --data_test DIV2K --model physics_sr --scale 2 --save physics_sr_x2 --save_results

The trained model can be found in ./experiment/physics_sr_x2/model

• To train all scales, you can modify the option(scale) of the command above.
• To test other benchmarks, you can modify the option(data_test) of the command above.
• To change the save root, you can modify the option(save) of the command above.

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

@inproceedings{pan2020physics_sr,
    title={Image Formation Model Guided Deep Image Super-Resolution},
    author={Jinshan Pan, Yang Liu, Deqing Sun, Jimmy Ren, Ming-Ming Cheng, Jian Yang, and Jinhui Tang},
    booktitle={AAAI},
    year={2020}
}

This code is built on EDSR(PyTorch). We thank the authors for sharing their codes of EDSR.