PFFNet
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Solution for NTIRE2018 Image Dehazing Challenge & ACCV2018 Kangfu Mei et al.
PFFNet
Our solution for NTIRE2018 Image Dehazing Challenge (20.549db for Indoor and 20.230db for Outdoor), final results could be refer at NTIRE2018. Futher version is accepted by ACCV2018 https://arxiv.org/pdf/1810.02283.pdf. All pretrained models can be found at Here
Preparation
Using data_argument to enchance the datasets, it will produce below datasets
$ python dara_argument.py --fold_A=IndoorTrainHzay --fold_B=IndoorTrainGT --fold_AB=IndoorTrain
IndoorTrain
\data hazy image
\label clear image
Train
Using default parameter to train
python train.py --cuda --gpus=4 --train=/path/to/train --test=/path/to/test --lr=0.0001 --step=1000
Test
python test.py --cuda --checkpoints=/path/to/checkpoint --test=/path/to/testimages
Citation
If you use the code in this repository, please cite our paper:
@inproceedings{mei2018pffn,
title={Progressive Feature Fusion Network for Realistic Image Dehazing},
author={Mei, Kangfu and Jiang, Aiwen and Li, Juncheng and Wang, Mingwen},
booktitle={Asian Conference on Computer Vision (ACCV)},
year={2018}
}