RESIDE

A Benchmark for Single Image Dehazing

Standard Version

News :

>Our extended version is available in RESIDE-β. Please feel free to utilize it.

Please check our updated manuscript for why and how we divide RESIDE into the standard and beta parts. Feel free to use them seperately or as one to evaluate your algorithm.

We're preparing a leaderboard and you're welcome to send in your results to be included in our website & manuscript.

Abstract

We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE sheds light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.

Overview

Dataset Examples:

Bibtex

@article{li2017reside,

title={RESIDE: A Benchmark for Single Image Dehazing},

author={Li, Boyi and Ren, Wenqi and Fu, Dengpan and Tao, Dacheng and Feng, Dan and Zeng, Wenjun and Wang, Zhangyang},

journal={arXiv preprint arXiv:1712.04143},

year={2017}

}

Download RESIDE-standard Dataset

ITS (Indoor Training Set):

(Dropbox): http://tinyurl.com/y98eo4w8

(Baidu Yun):http://tinyurl.com/yaohd3yv Passward: g0s6

SOTS (Synthetic Objective Testing Set):

(Dropbox): http://t.cn/RQ34zUi

(Baidu Yun): https://pan.baidu.com/share/init?surl=SSVzR058DX5ar5WL5oBTLg Passward: s6tu

HSTS (Hybrid Subjective Testing Set):

(Dropbox) http://tinyurl.com/y7keuhvx

(Baidu Yun)https://pan.baidu.com/s/1cl1exWnaFXe3T5-Hr7TJIg Passward: vzeq

Note: For Baidu Yun Link, you might need to refresh again or copy the link to a new webpage if it fails to redirect.

请注意:如果您无法直接点击百度云链接访问,请开启一个新的网页并复制链接到地址栏。

How to Evaluate PSNR, SSIM

To standardize the evaluation of PSNR and SSIM, here is our calculation code in RESIDE paper

(Both PSNR and SSIM from Matlab 2017)

PSNR.m SSIM.m

Contact

Boyi Li (boyilics@gmail.com)

Wenqi Ren (rwq.renwenqi@gmail.com)

Zhangyang Wang (atlaswang@tamu.edu)