MSBDN-DFF-master_gateghv_msbdn_msdn_MSBDN-DFF中GRES_msbdnDFF_

# MSBDN-DFF The source code of CVPR 2020 paper **"Multi-Scale Boosted Dehazing Network with Dense Feature Fusion"** by [Hang Dong](https://sites.google.com/view/hdong/%E9%A6%96%E9%A1%B5), [Jinshan Pan](https://jspan.github.io/), [Zhe Hu](https://zjuela.github.io/), Xiang Lei, [Xinyi Zhang](http://xinyizhang.tech), Fei Wang, [Ming-Hsuan Yang](http://faculty.ucmerced.edu/mhyang/) ## Dependencies * Python 3.6 * PyTorch >= 1.1.0 * torchvision * numpy * skimage * h5py * MATLAB ## Test 1. Download the [Pretrained model on RESIDE](https://drive.google.com/open?id=1da13IOlJ3FQfH6Duj_u1exmZzgXPaYXe) and [Test set](https://drive.google.com/open?id=1qZlnJN4ybjunc2BGh6kjOUfFdVxuNS-P) to ``MSBDN-DFF/models`` and ``MSBDN-DFF/``folder, respectively. 2. Run the ``MSBDN-DFF/test.py`` with cuda on command line: ```bash MSBDN-DFF/$python test.py --checkpoint path_to_pretrained_model ``` 3. The dehazed images will be saved in the directory of the test set. ## Train The training scripts will be coming soon. ## Citation If you use these models in your research, please cite: @conference{MSBDN-DFF, author = {Hang, Dong and Jinshan, Pan and Zhe, Hu and Xiang, Lei and Fei, Wang and Ming-Hsuan, Yang}, title = {Multi-Scale Boosted Dehazing Network with Dense Feature Fusion}, booktitle = {CVPR}, year = {2020} }