MI-Net

This is the code for our paper "Implicit Euler ODE Networks for Single-Image Dehazing". [PAPER]

Citation

If you find MI-Net useful in your research, please consider citing:

@inproceedings{shen2020implicit,
  title={Implicit Euler ODE Networks for Single-Image Dehazing},
  author={Shen, Jiawei and Li, Zhuoyan and Yu, Lei and Xia, Gui-Song and Yang, Wen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={218--219},
  year={2020}
}

Benchmark Results

Prerequistes

• Ubuntu 16.04
• Pytorch 1.0.1

Train

python main.py

Before the training process, you have to reset the parameters in main.py, for an instance, the path of your datasets and result.

For the training dataset, you can use images directly with our dataset building functions in create.py (Note that the names between input and ground-truth have to be corresponding!) or use the dataset in PyTorch form.

[RESIDE_Dataset]

[Middlebury_Dataset]

Test

python test_dehazy.py

You can use the pretrained model we provided to test the images. Also, you have to reset the path parameters before the image test.