DehazeNet: An End-to-End System for Single Image Haze Removal
Open accessAbstract
Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.
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Recovering the clear image from a single hazy input is an ill-posed problem that can be addressed by both prior-based and learning-based methods.
“Recovering the clear image from a single hazy input is also an illposed problem, which can be addressed by both prior-based and learning-based methods [149], [150], [151].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [150]
Cai et al. propose the first deep CNN model for image dehazing, which outperforms traditional prior-based methods by leveraging the powerful representation capacity of CNNs.
“Cai et al. propose the first deep CNN model for image dehazing, which outperforms traditional prior-based methods by leveraging the powerful representation capacity of CNNs [150].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [150]
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- Authors
- Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D.
- Affiliations
- South China University of Technology (CN)
- University of Technology Sydney (AU)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2016-11
- Volume
- 25
- Issue
- 11
- Pages
- 5187-5198
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2256362396
- Cited by
- 3,367
- References
- 51
- Citations by year
- 2026: 319 · 2025: 463 · 2024: 500 · 2023: 442 · 2022: 410 · 2021: 393 · 2020: 313 · 2019: 257 · 2018: 193 · 2017: 68 · 2016: 7 · 2014: 1
- FWCI
- 119.02
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Founding of China (61171142, 61401163)
- Science and Technology Planning Project of Guangdong Province of China (2011A010801005, 2014B010111003, 2014B010111006)
- Guangzhou Key Laboratory of Body Data Science (201605030011)
- Australian Research Council Projects (FT-130101457, DP-140102164, LE-140100061)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- single image dehazing, atmospheric scattering model, end-to-end neural network, nonlinear activation functions
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