A Benchmark for Single Image Dehazing

News :

>We have updated ITS, SOTS dataset ( Baidu Yun Link) and HSTS(both Dropbox and Baidu Yun Link). Please feel free to utilize it.

>We have updated SOTS dataset (both Dropbox and Baidu Yun Link). Please feel free to utilize it.


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 shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.


Dataset Examples:



title={Benchmarking Single-Image Dehazing and Beyond},

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

journal={IEEE Transactions on Image Processing},







Download RESIDE Dataset

ITS (Indoor Training Set):


(Baidu Yun): Passward: tqyh

OTS (Outdoor Training Set):


(Baidu Yun): Passward: 5vss

SOTS (Synthetic Objective Testing Set):


(Baidu Yun): Passward: s6tu

RTTS (Real-world Task-Driven Testing Set):


(Baidu Yun) Passward: n3v8

HSTS (Hybrid Subjective Testing Set):


(Baidu Yun) Passward: vzeq

Unannotated Real-world Hazy Images:


(Baidu Yun) : Password:vt99

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


How to Use RTTS

RTTS is in the same format as VOC 2007, organized as follows:

-- HazeDetection

-- Annotations

-- *** .xml

-- ImageSets

-- Main

-- test.txt

-- JPEGImages

-- *** .png

Here is a demo script to visualize the annotations:, which shows how to get access to the annotated bounding boxes.

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 2013)