• This is the official repository of the paper "Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution" from IEEE Transactions on Image Processing 2021. [Paper Link][PDF Link]
• We have conducted a live streaming on Jishi Platform, the Powerpoint file can be downloaded from [PPT Link].

• Python >= 3.5
• PyTorch >= 0.4.1 is recommended
• opencv-python
• tqdm
• Matlab

The training data and testing data is from the [SICE dataset]. Or you can download the datasets from our [Google Drive Link].

1. Clone this repository:

   git clone https://github.com/ytZhang99/CF-Net.git
   

2. Place the low-resolution over-exposed images and under-exposed images in dataset/test_data/lr_over and dataset/test_data/lr_under, respectively.
3. Run the following command for 2 or 4 times SR and exposure fusion:

   python main.py --test_only --scale 2 --model model_x2.pth
   python main.py --test_only --scale 4 --model model_x4.pth
   

4. Finally, you can find the Super-resolved and Fused results in ./test_results.

For some reason, we haven't released the training code.

If you want to get access to the training code, you can email [email protected] for the training methods and materials.

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

@article{deng2021deep,
  title={Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution.},
  author={Deng, Xin and Zhang, Yutong and Xu, Mai and Gu, Shuhang and Duan, Yiping},
  journal={IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society},
  year={2021}
}