Single Image Dehazing

Includes 950 real-world underwater images, 890 of which have the corresponding reference images. Source: An Underwater Image Enhancement Benchmark Dataset and Beyond

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. Source: RESIDE

The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images. Source: Single image dehazing for a variety of haze scenarios using back projected pyramid network Image Source: https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/

NN-HAZE is an image dehazing dataset. Since in many real cases haze is not uniformly distributed NH-HAZE, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images. This is the first non-homogeneous image dehazing dataset and contains 55 outdoor scenes. The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates the real conditions of hazy scenes. Source: NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images

A dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images. Source: Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

The D-HAZY dataset is generated from NYU depth indoor image collection. D-HAZY contains depth map for each indoor hazy image. It contains 1400+ real images and corresponding depth maps used to synthesize hazy scenes based on Koschmieder’s light propagation mode Source: C2MSNet: A Novel approach for single image haze removal Image Source: https://www.semanticscholar.org/paper/D-HAZY%3A-A-dataset-to-evaluate-quantitatively-Ancuti-Ancuti/9451d0b1bfbba5f3e19c083866f1394aabf7d06c

The SMOKE dataset is a dataset for fog/smoke removal. There are 110 self-collected fog/smoke images and their clean pairs. There are 12 other pairs of fog data for evaluation.

A small-scale real-world dataset containing hazy/dusty industrial images and their clean ground truth counterparts. Designed for evaluating deep learning models for dust removal and image dehazing in industrial environments. Collected and fine-tuned by Moshtaghioun et al., 2025.