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基于生成对抗网络的电力设施图像去雾方法

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电力设施监控可有效避免重大电力事故的发生,但在恶劣天气下大雾等自然现象的影响会使电力设施监控图像模糊不清,难以正常工作.针对该问题,提出了一种基于生成对抗网络(GAN)的电力设施图像去雾方法,其通过将监控彩色图像分解为RGB三通道来进行深度去雾.设计了新型联合损失函数以加强生成对抗网络的学习能力.在电力图像去雾数据集上的实验验证得出,与对比方法相比,所提出的方法的PSNR提升了1.1,SD提升了2.97,具备更好的图像去雾性能.
Generative Adversarial Network-based Image Dehazing of Electric Power Facilities
Power facility monitoring can effectively avoid the occurrence of major power accidents,but under the influence of natural phenomena such as heavy fog in bad weather,the monitoring images of power facilities are blurred and difficult to work appropriately.Aiming at this problem,this paper proposes a dehazing method for power facility images based on generative adversarial network (GAN).This method performs deep dehazing by decomposing the surveillance color image into three RGB channels.Moreover,a novel joint loss function is designed to enhance the learning ability of generative ad-versarial networks.Extensive experimental verification on power image dehazing dataset confirms better image dehazing performance of the proposed method compared with the comparative methods,as the PSNR and SD are improved by 1 .1 and 2 .97 ,respectively.

feature enhancementgenerative adversarial networkpower imageimage dehazingimage processing

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广西博联信息通信技术有限责任公司,广西 南宁 530000

特征增强 生成对抗网络 深度学习 图像去雾 图像处理

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(7)
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