Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal Asian Conference on Computer Vision (ACCV'2022)
[Paper]
[Supp]
${FogRemoval}
|-- Dataset_day
|-- [Smoke](https://www.dropbox.com/home/badweather/ACCV2022_defog/Dataset_day/Smoke)
|-- train (110 pairs)
|-- hazy
|-- clean
|-- [test] (12 pairs)
|-- hazy
|-- clean
[SMOKE Train] [SMOKE Test] [Ours Results]
[Dense-HAZE] [NH-HAZE] [O-HAZE]
Download the pre-trained NH-HAZE model, put in results/NH-HAZE/model/NH-HAZE_params_0100000.pt
python main_test.py --datasetpath [path_to_NH-HAZE dataset]
${FogRemoval}
|-- Dataset_day
|-- Cityscapes
|-- disparity
|-- leftImg8bit
|-- train (2,975 pairs)
|-- hazy
|-- clean
|-- test (1,525 pairs)
|-- hazy
|-- clean
|-- generate_haze_cityscapes.m
Run the Matlab code to generate Synthetic Fog Cityscapes pairs:
Cityscapes/generate_haze_cityscapes.m
If smoke data is useful for your research, please cite our paper.
@InProceedings{Jin_2022_ACCV,
author = {Jin, Yeying and Yan, Wending and Yang, Wenhan and Tan, Robby T.},
title = {Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal},
booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
month = {December},
year = {2022},
pages = {2041-2058}
}