摘要
提出了一种基于深度学习的单幅图像去雾算法。利用卷积神经网络,通过学习雾天图像与清晰图像的YUV(Y代表的是亮度,UV代表的是彩度)各个通道之间的映射关系实现去雾。网络结构由两个相同的特征模块组成,主要包括多尺度卷积、卷积和跳跃连接结构。实验结果表明,无论是针对合成雾天图像数据集还是针对自然雾天图像数据集,所提去雾算法恢复的图像皆具有较高的清晰度和对比度,在主观评价和客观评价上均优于其他对比算法。
A single-image defogging algorithm based on deep learning is proposed.The convolutional neural network achieves defogging by learning the mapping relationship among the YUV(Y is luminance,UV is chrominance)channels of the foggy and clear images.The network structure comprises two identical feature modules,which mainly include multi-scale convolution,convolution and skip-connection frameworks.The experimental results show that the proposed algorithm can be used to restore images with high resolution and high contrast,regardless of the datasets with synthetic or natural fog images.Furthermore,a comparative evaluation of this algorithm with the existing algorithms confirms its superior performance both subjectively and objectively.
作者
赵建堂
Zhao Jiantang(College of Mathematics and Information Science,Xianyang Normal University,Xianyang,Shaanxi 712000,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第11期138-145,共8页
Laser & Optoelectronics Progress
基金
陕西省教育厅项目(11JK0513)
陕西省教育厅自然科学基金项目(15JK2157)
关键词
图像处理
图像去雾
深度学习
大气散射模型
图像恢复
多尺度卷积
image processing
image defogging
deep learning
atmospheric scattering model
image restoration
multi-scale convolution
