- 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 Extreme Mart 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].
- Clone this repository:
git clone https://github.com/ytZhang99/CF-Net.git
- Place the low-resolution over-exposed images and under-exposed images in
dataset/test_data/lr_over
anddataset/test_data/lr_under
, respectively. - 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
- 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}
}