An adaptive defogging method for aerial images on rainy days based on deep learning

Xinyu Shao; Liang Wang; Yao Yao

doi:10.1117/12.3026378

15 March 2024 An adaptive defogging method for aerial images on rainy days based on deep learning

Xinyu Shao, Liang Wang, Yao Yao

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751R (2024) https://doi.org/10.1117/12.3026378
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

Dehazing rainy aerial images poses a challenging problem, as traditional dehazing methods have limited effectiveness under complex lighting conditions and various scenes. Therefore, an adaptive dehazing method for rainy aerial images based on deep learning is proposed. This method utilizes deep learning networks to learn the relationship between haze and foreground information in aerial images and removes the impact of haze by restoring clear images. To adapt to different lighting conditions and scenes, adaptive techniques are introduced in the network design and optimization process to achieve better adaptability and robustness. Experimental results demonstrate that this method has achieved significant improvements in dehazing quality and exhibits strong robustness across various scenes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyu Shao, Liang Wang, and Yao Yao "An adaptive defogging method for aerial images on rainy days based on deep learning", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751R (15 March 2024); https://doi.org/10.1117/12.3026378

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KEYWORDS

Image processing

Rain

Deep learning

Fiber optic gyroscopes

Image quality

Image fusion

Education and training

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