Video dehazing based on CNN
Pages 56 - 60
Abstract
The appearance of outdoor images is easily affected by natural phenomena such as fog and dust, which reduces contrast and color distortion. Video dehazing has a wide range of real-time applications, but the challenges mainly come from large amount of computation and bad real-time performance. In this paper, we propose a video dehazing system which is an end-to-end network based on CNN (Convolutional Neural Network). The dehazing algorithm learns the scene transmission and the global atmospheric light simultaneously, which simplifies the dehaze process and improves the real-time performance. Finally, we process videos through combining the end-to-end dehaze network and bicubic interpolation algorithm, and obtain satisfactory results. The experiment results demonstrate that the proposed method performs favorably against the state-of-the-art methods on both quantitative and qualitative evaluation.
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Index Terms
- Video dehazing based on CNN
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Information & Contributors
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Published In
January 2020
191 pages
ISBN:9781450376648
DOI:10.1145/3381271
- Conference Chair:
- Wanyang Dai,
- Program Chairs:
- Xiangyang Hao,
- Ramayah T,
- Fehmi Jaafar
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Sponsors
- NJU: Nanjing University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 February 2020
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Author Tags
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- Research-article
Funding Sources
- Beijing Institute of Technology
Conference
ICMIP 2020
Sponsor:
- NJU
ICMIP 2020: 2020 5th International Conference on Multimedia and Image Processing
January 10 - 12, 2020
Nanjing, China
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