PFFNet-PyTorch

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This repository is an official PyTorch implementation of the paper "Progressive Feature Fusion Network for Realistic Image Dehazing". (ACCV 2018)

PFFNet-PyTorch 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# PFFNet-PyTorch
### This repository is an official PyTorch implementation of the paper "Progressive Feature Fusion Network for Realistic Image Dehazing".

Original link to this project: [PFFNet](https://github.com/MKFMIKU/PFFNet).

Paper can be download from <a href="https://www.jianguoyun.com/p/DSNFu1MQ19ySBxic-8oB">PFFNet</a>

PFFNet is a solution for the NTIRE2018 image defogging challenge (20.549db for Indoor and 20.230db for Outdoor), final results could be found at [NTIRE2018](http://www.kfm.cat/pdf/ntire-2018-dehazing-challenge-report.pdf). 
We won the six place and awarded "Honorable Mention Award".

Improved version was accepted by ACCV2018.

All pretrained models can be found at: [Here](https://drive.google.com/open?id=198ZP8yV3gl49IdSZHEz9SMmTvppt_a9v)

---------

## Preparation
Using data_argument.py to enchance the datasets, it will produce below datasets
```bash
$ 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
```bash
python train.py --cuda --gpus=4 --train=/path/to/train --test=/path/to/test --lr=0.0001 --step=1000
```

## Test
```bash
python test.py --cuda --checkpoints=/path/to/checkpoint --test=/path/to/testimages
```

## Results

<img src="images/result_1.jpg">

--------

## 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}
}
```

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