Dehazing algorithm based on interval estimation and adaptive constraints of transmittance

YANG Yan; ZHANG Jinlong; ZHANG Haowen

doi:10.13700/j.bh.1001-5965.2020.0547

Volume 48 Issue 1

Jan. 2022

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Abstract

References

Journal of Beijing University of Aeronautics and Astronautics > 2022 > 48(1): 15-26.

XU Hua-ping, ZHOU Yin-qing, CHEN Jie, et al. Interferometric SAR Phase Noise Reduction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2001, 27(1): 16-19. (in Chinese)

Citation:

YANG Yan, ZHANG Jinlong, ZHANG Haowenet al. Dehazing algorithm based on interval estimation and adaptive constraints of transmittance[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 15-26. doi: 10.13700/j.bh.1001-5965.2020.0547(in Chinese)

XU Hua-ping, ZHOU Yin-qing, CHEN Jie, et al. Interferometric SAR Phase Noise Reduction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2001, 27(1): 16-19. (in Chinese)

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Dehazing algorithm based on interval estimation and adaptive constraints of transmittance

doi: 10.13700/j.bh.1001-5965.2020.0547

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Funds:

National Natural Science Foundation of China 61561030

College Industry Support Plan Project of Gansu Provincial Department of Education 2021CYZC-04

Excellent Graduate Student Innovation Star Project in Gansu Province 2021CXZX-607

Research Fund of Teaching Reform Project of Lanzhou Jiaotong University JG201928

More Information

Corresponding author: YANG Yan, E-mail: yangyantd@mail.lzjtu.cn
Received Date: 25 Sep 2020
Accepted Date: 06 Feb 2021
Publish Date: 20 Jan 2022

Abstract

Abstract

In order to solve the problems such as insufficient transmittance estimation and color cast of results of dehazing algorithms, an image restoration algorithm based on minimum channel interval estimation and transmittance adaptive constraint model is proposed. Firstly, bright channel of hazy image is obtained by using maximum operation of different sizes, and average value processing and frequency domain filtering are combined to get the atmospheric light estimation. Secondly, starting from the atmospheric imaging theory, minimum channel of hazy image is regarded as a constraint, then upper and lower boundaries of minimum channel of hazy image are fitted by plane model and adaptive mapping model respectively, and minimum channel of dehazed image and initial transmittance estimation are obtained. Finally, the initial transmittance can be refined by filter smoothing and adaptive boundary constraints to obtain the optimized transmittance, and according to atmospheric scattering model, restoration results are obtained. Experiments show that the restoration results of the proposed algorithm have natural colors, appropriate brightness, thorough degree of dehazing, rich detailed information and low time complexity, which effectively solves the problems of insufficient transmittance estimation and color cast.
- image restoration,
- frequency domain filtering,
- plane constraint,
- function mapping,
- interval estimation,
- adaptive boundary constraint

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References(23)

References

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