search

zhilin007 / FFA-Net

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
601 stars 141 forks source link
aaai2020 ffa ffanet pytorch single-image-dehazing

readme

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing (AAAI 2020)

Official implementation.

by Xu Qin, Zhilin Wang et al. Peking University and Beijing University of Aeronautics & Astronautics.

Citation

@inproceedings{qin2020ffa,
title={FFA-Net: Feature fusion attention network for single image dehazing},
author={Qin, Xu and Wang, Zhilin and Bai, Yuanchao and Xie, Xiaodong and Jia, Huizhu},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={07},
pages={11908--11915},
year={2020}
}

Dependencies and Installation

Datasets Preparation

Dataset website:RESIDE ; Paper arXiv version:[RESIDE: A Benchmark for Single Image Dehazing]

FILE STRUCTURE ``` FFA-Net |-- README.md |-- net |-- data |-- RESIDE |-- ITS |-- hazy |-- *.png |-- clear |-- *.png |-- OTS |-- hazy |-- *.jpg |-- clear |-- *.jpg |-- SOTS |-- indoor |-- hazy |-- *.png |-- clear |-- *.png |-- outdoor |-- hazy |-- *.jpg |-- clear |-- *.png ```

Metrics update

Methods Indoor(PSNR/SSIM) Outdoor(PSNR/SSIM)
DCP 16.62/0.8179 19.13/0.8148
AOD-Net 19.06/0.8504 20.29/0.8765
DehazeNet 21.14/0.8472 22.46/0.8514
GFN 22.30/0.8800 21.55/0.8444
GCANet 30.23/0.9800 -/-
Ours 36.39/0.9886 33.57/0.9840

Usage

Train

Remove annotation from main.py if you want to use tensorboard or view intermediate predictions

If you have more computing resources, expanding bs, crop_size, gps, blocks will lead to better results

train network on ITS dataset

 python main.py --net='ffa' --crop --crop_size=240 --blocks=19 --gps=3 --bs=2 --lr=0.0001 --trainset='its_train' --testset='its_test' --steps=500000 --eval_step=5000

train network on OTS dataset

 python main.py --net='ffa' --crop --crop_size=240 --blocks=19 --gps=3 --bs=2 --lr=0.0001 --trainset='ots_train' --testset='ots_test' --steps=1000000 --eval_step=5000

Test

Trained_models are available at baidudrive: https://pan.baidu.com/s/1-pgSXN6-NXLzmTp21L_qIg with code: 4gat

or google drive: https://drive.google.com/drive/folders/19_lSUPrpLDZl9AyewhHBsHidZEpTMIV5?usp=sharing Put models in the net/trained_models/folder.

Put your images in net/test_imgs/

 python test.py --task='its or ots' --test_imgs='test_imgs'

Samples