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This repository is an official PyTorch implementation of the paper "Progressive Feature Fusion Network for Realistic Image Dehazing". (ACCV 2018)
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MIVRC/PFFNet-PyTorch
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This repository is an official PyTorch implementation of the paper "Progressive Feature Fusion Network for Realistic Image Dehazing".
Original link to this project:PFFNet.
Paper can be download fromPFFNet
PFFNet is a solution for the NTIRE2018 image defogging challenge (20.549db for Indoor and 20.230db for Outdoor), final results could be found atNTIRE2018.We won the six place and awarded "Honorable Mention Award".
Improved version was accepted by ACCV2018.
All pretrained models can be found at:Here
Using data_argument.py 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
Using default parameter to train
python train.py --cuda --gpus=4 --train=/path/to/train --test=/path/to/test --lr=0.0001 --step=1000
python test.py --cuda --checkpoints=/path/to/checkpoint --test=/path/to/testimages
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}}