This dataset is present for Image Dehazing task. It contains image pairs from various different image dehazing challenges and open source. The end goal is to use this dataset to train an end to end deep learning model which can effectively handle the task of dehazing. The data was procured from the following locations. - RESIDE (ITS and SOTS) - I-Haze (NTIRE-2018) - O-Haze (NTIRE-2018) - Dense-Haze (NTIRE-2019) - NH-Haze (NTIRE-2020)

Dataset

This dataset was created by 刘啸飞lxfbystander

Contents

Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and the loss of important information during network sampling, resulting in incomplete or structurally flawed dehazing outcomes. To address these challenges, we propose a multi-level perception fusion dehazing network (MPFDN) that effectively integrates feature information across different scales, expands the perceptual field of the network, and fully extracts the spatial background information of the image. Moreover, we employ an error feedback mechanism and a feature compensator to address the loss of features during the image dehazing process. Finally, we subtract the original hazy image from the generated residual image to obtain a high-quality dehazed image. Based on extensive experimentation, our proposed method has demonstrated outstanding performance not only on synthesizing dehazing datasets, but also on non-homogeneous haze datasets.

City Haze

## Overview

City Haze is a dataset for object detection tasks - it contains Dehaze annotations for 4,322 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.

Haze

## Overview

Haze is a dataset for classification tasks - it contains Haze annotations for 206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Hazy conditions make computer vision tasks difficult, especially when working with real-world videos. Although many video dehazing algorithms have been proposed, their progress is limited by the lack of large, real-world hazy video datasets. To fill this gap, we introduce HazeBench, a dataset compiled from real-world footage captured under various environmental conditions. Unlike many existing datasets, HazeBench does not use ground-truth clear videos, making it more representative of real haze situations. The dataset includes 153 videos (1-15 seconds each) and 65,078 images, grouped into five scene categories: Indoor, Mountains, Night, Road, and Rural Areas. We describe how the dataset was collected, highlight its main features, and show its usefulness through benchmark experiments with video dehazing algorithms. HazeBench provides a valuable resource for developing and testing dehazing methods and supports more reliable computer vision applications in real-world environments.

Dataset

This dataset was created by Lithesh Shetty

Contents

Haze Dataset

## Overview

Haze Dataset is a dataset for object detection tasks - it contains Ship Airplane annotations for 441 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

FINEDUST is an image dataset for assessing image dehazing algorithms on scenes degraded by fine dust/yellow dust. It consists of 30 real images collected from the Internet.

Remote sensing image dehazing results on StateHaze1K and RS-Haze datasets

Published in ACM Multimedia 2024, Melbourne, Australia HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing [Paper] Md Tanvir Islam 1, Nasir Rahim 1, Saeed Anwar 2, Muhammad Saqib 3, Sambit Bakshi 4, Khan Muhammad 1, * | 1. Sungkyunkwan University, South Korea | 2. ANU, Australia | 3. CSIRO, Australia | 4. NIT Rourkela, India || *Corresponding Author |

  IMPORTANT UPDATES

2025/07/23 | The Satellite version is also uploaded now. 2025/02/28 | We will try our best to make… See the full description on the dataset page: https://huggingface.co/datasets/tanvirnwu/HazeSpace2M.

Dataset

This dataset was created by Philip Hofmann

Contents

Quantitative evaluations on outdoor hazy SOTS images.

This dataset contains the predicted prices of the asset Haze over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

wandering-tiger/Vehicle-Detection-in-haze-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Haze Machine Market Outlook

According to our latest research, the global haze machine market size in 2024 stands at USD 478 million, with a robust compound annual growth rate (CAGR) of 5.7% projected from 2025 to 2033. By the end of 2033, the market is expected to reach approximately USD 794 million, driven by increasing demand across entertainment, events, and commercial sectors. This growth is underpinned by the rising adoption of haze machines in live performances, theatrical productions, and the expanding entertainment industry, as well as technological innovations improving machine efficiency and safety.

A primary growth factor for the haze machine market is the surging demand for immersive visual effects in live entertainment and corporate events. As productions increasingly seek to enhance audience experiences, haze machines have become essential for creating atmospheric depth and highlighting lighting effects. The proliferation of music festivals, concerts, and large-scale events, particularly in emerging markets, has further spurred demand. Additionally, the integration of advanced features such as remote control, programmable settings, and eco-friendly formulations in haze machines has attracted a broader customer base, including event organizers and stage designers who prioritize both performance and sustainability.

Another significant driver is the expansion of the global film, television, and photography industries. Haze machines are widely utilized in these fields to create mood, simulate environmental conditions, and achieve specific visual effects that are difficult to replicate digitally. The growing investment in content creation, fueled by the rise of streaming platforms and increased consumer appetite for high-quality productions, has led to a steady increase in demand for haze machines. Furthermore, the trend towards hybrid and virtual events, accelerated by global shifts in event management post-pandemic, has reinforced the importance of atmospheric effects, further boosting market growth.

Technological advancements and stringent safety regulations have also played pivotal roles in shaping the haze machine market. Manufacturers are focusing on developing products that are not only more efficient but also safer for both users and the environment. The introduction of water-based haze machines, which produce less residue and are less harmful to health, has gained significant traction. Additionally, the growing awareness of occupational health and safety standards in entertainment venues and industrial applications has driven the adoption of certified, low-emission haze machines. This trend is expected to continue, as end-users increasingly prioritize compliance and sustainability.

Regionally, North America remains the largest market for haze machines, owing to its well-established entertainment industry and frequent hosting of large-scale events. However, the Asia Pacific region is witnessing the fastest growth, propelled by rapid urbanization, increasing disposable incomes, and a burgeoning events sector in countries like China, India, and Japan. Europe also represents a significant market, supported by its vibrant cultural scene and strong regulatory framework for safety and environmental standards. Latin America and the Middle East & Africa are emerging as promising markets, with growing investments in tourism, hospitality, and entertainment infrastructure contributing to the expanding adoption of haze machines.

Product Type Analysis

The haze machine market, segmented by product type into water-based and oil-based haze machines, demonstrates distinct growth trajectories and adoption patterns. Water-based haze machines are increasingly favored due to their environmentally friendly composition and minimal health risks. These machines use water-soluble fluids, resulting in less residue and a cleaner operation, which is particularly important in enclosed spaces and venues with stringent air quality regulations. As sustainability becomes a central concern for event organizers and venue operators, the demand for water-based haze machines is expected to outpace that for their oil-based counterparts. Furthermore, advancements in fluid formulation and machine design have improved the longevity and consistency of haze effects, making water-based options more attractive for both small and large-scale productions.

Oil-based haze machines, while stil

Quantitative evaluations on indoor hazy SOTS images.

Dataset

This dataset was created by elharrauss

Contents

This dataset is about books. It has 5 rows and is filtered where the book is Haze. It features 7 columns including author, publication date, language, and book publisher.