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.
Sea-Land segmentation based on surveillance images is an important research content for real-time coast monitoring. However, the complex weather and environmental makes the segmentation of sea-land is a difficult task. Although previous deep learning methods based on convolutional neural networks have achieved excellent results in semantic segmentation, and there has been some work using deep convolutional neural networks for Sea-Land segmentation but we hope that the image segmentation model can achieve more accurate results in sea and land segmentation. In our method, we propose a novel sea-land segmentation framework called Multi Sea-Land U-net (MSLUnet), the framework base on a multi-scale. The proposed MSLUnet is mainly composed of a multi-scale layer and U-Net convolutional network. The multi-scale input layer constructs an image pyramid to accept multiple levels of image data in the network model. U-shaped convolutional networks are used as the back-bone network structure to learn rich hierarchical representations. Experimental results show that compared with other architectures, MSLUnet has achieved good performance.
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