Paper
10 November 2020 Coastal zone image dehazing network based on feature fusion and adversarial training
Author Affiliations +
Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158407 (2020) https://doi.org/10.1117/12.2579200
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China
Abstract
The quality of the images collected by the coastal zone video surveillance equipment is seriously degraded due to the sea fog, which directly affects the analysis of the image. Therefore, the study of the costal image dehazing method is of great significance to the related research of the coastal zone. Costal image has the characteristics of large sky area and monotonous color. The traditional method based on atmospheric scattering physics model is not suitable for this kind of image for block effect and color distortion. In this paper, we introduce the generative adversarial mechanism into sea fog image defogging, and propose a coastal image dehazing network based on it. The proposed model includes a generative network and a discriminative model, and is trained by adversarial mechanism. The generative model is composed of multi-scale feature extraction module and residual connection module. The discriminative network consists of two subnetworks of receptive field of different sizes.
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Xiaoyu Wu, Gong Chen, Xiangqian Ding, KeZhen Xie, and Guanqun Wei "Coastal zone image dehazing network based on feature fusion and adversarial training", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158407 (10 November 2020); https://doi.org/10.1117/12.2579200
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KEYWORDS
Image fusion

Convolution

Feature extraction

Atmospheric modeling

Image enhancement

Air contamination

Coastal modeling

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