A novel CNN model for single image dehazing

Zhipeng Li; Cheng Liu; Yi Li; Xiaobing Zhong; Man Yuan; Xinwei Wan

doi:10.1117/12.3050049

8 November 2024 A novel CNN model for single image dehazing

Zhipeng Li, Cheng Liu, Yi Li, Xiaobing Zhong, Man Yuan, Xinwei Wan

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162W (2024) https://doi.org/10.1117/12.3050049
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Inspiring by the atmosphere shattering model, we deduced a novel transform model for image dehazing. Based on the prosed model we designed a light easy-training CNN network for end-to-end image dehazing tasks. We trained our module on RESIDE dataset and tested it on O-HAZY, I-HAZY and NH-HAZE datasets. The results suggest that comparing with AOD-Net and Dehaze-Net our proposed method gives a better performance in image dehazing. With the experiments, we also analyzed the mechanism of blue shifting and explained how our module would help in solving the blue shifting problem. As a light model, it can be combined with other detection network such as YOLO or F-RCNN easily to maintain complex tasks in future researches.

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

Citation Download Citation

Zhipeng Li, Cheng Liu, Yi Li, Xiaobing Zhong, Man Yuan, and Xinwei Wan "A novel CNN model for single image dehazing", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162W (8 November 2024); https://doi.org/10.1117/12.3050049

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KEYWORDS

Atmospheric modeling

Air contamination

Feature extraction

Image enhancement

Light scattering

Scattering

Education and training

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