FAMED-Net: A Fast and Accurate Multi-Scale End-to-End Dehazing Network
Open accessAbstract
Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks. However, it remains challenging due to its ill-posed nature. Existing dehazing models tend to suffer from model overcomplexity and computational inefficiency or have limited representation capacity. To tackle these challenges, here, we propose a fast and accurate multi-scale end-to-end dehazing network, called FAMED-Net, which comprises encoders at three scales and a fusion module to efficiently and directly learn the haze-free image. Each encoder consists of cascaded and densely connected point-wise convolutional layers and pooling layers. Since no larger convolutional kernels are used and features are reused layer-by-layer, FAMED-Net is lightweight and computationally efficient. Thorough empirical studies on public synthetic datasets (including RESIDE) and real-world hazy images demonstrate the superiority of FAMED-Net over other representative state-of-the-art models with respect to model complexity, computational efficiency, restoration accuracy, and cross-set generalization. The code will be made publicly available.
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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 [151]
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Research works printed but not linked: 1
- [54][54] M. Elad, 'Retinex by two bilateral filters,' in International Conference on Scale-Space Theories in Computer Vision . Springer, 2005, pp. 217229. Fig. 10: Illustration of generating illumination unbalanced image. (a) A clear image in RESIDE [3]. (b) The generated illumination unbalanced image according to the non-linear mapping in (c). (c) An exemplar non-linear mapping fitted from the randomly selected control points. <!-- image -->
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Self-citation
- Authors
- Zhang, J., & Tao, D.
- Affiliations
- The University of Sydney (AU)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2020
- Volume
- 29
- Pages
- 72-84
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2955731188
- Cited by
- 196
- References
- 56
- Citations by year
- 2026: 8 · 2025: 24 · 2024: 29 · 2023: 30 · 2022: 43 · 2021: 36 · 2020: 20 · 2019: 6
- FWCI
- 11.75
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council (FL-170100117, DP-180103424, IH-180100002)
- National Natural Science Foundation of China (61806062, 61873077)
- 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, multi-scale network, lightweight neural networks, feature fusion module, computational efficiency
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