基于深度学习的遥感图像去雾算法  

Remote sensing image haze removal algorithm using deep learning

作　　者：李玉峰 任静波 黄煜峰 Li Yufeng;Ren Jingbo;Huang Yufeng(College of Electronic&Information Engineering,Shenyang Aerospace University,Shenyang 110136,China)

机构地区：[1]沈阳航空航天大学电子信息工程学院,沈阳110136

出　　处：《计算机应用研究》2021年第7期2194-2199,共6页Application Research of Computers

基　　金：国家高分专项资助项目(70-Y40-G09-9001-18/20);辽宁省自然科学基金资助项目(20180550334);国家教育部科学技术研究重点项目(2017A02002);辽宁省教育厅资助项目(L201701,L201735)。

摘　　要：针对遥感图像由于雾霾的存在导致图像清晰度下降的问题,提出了一种基于深度学习的图像去雾算法。首先将原有大气散射模型进行变形得到一个端到端的去雾模型,再将多个未知参数统一在一个参数中,运用多尺度卷积神经网络对未知参数进行估计,最后将参数估计值代入去雾模型中得到无雾图像。针对无参考图像数据集,先运用现有数据集对网络进行初步训练,再加入自建数据集对网络进行二次训练。实验结果表明,与相关去雾算法进行对比,该算法在视觉效果和客观指标上都有不同程度的提高,有效提升了遥感图像在雾霾天气状况下的清晰度。This paper presented an image dehazing method based on deep learning to solve the problem that the remote sensing images had reduced image sharpness due to haze.Firstly,this algorithm distorted the original atmospheric scattering model to get an end-to-end defrosting model.Then,it unified several unknown parameters to one parameter and estimated the unknown parameters by using multiscale convolution neural network.Finally,it brought the parameter estimates into the defrosting model to get a haze-free image.For no reference image dataset,this paper used the existing dataset to preliminary train the network,then added the self-built dataset to secondary train the network.The experimental results show that,compared with the related defrosting algorithms,the proposed algorithm improves the visual effect and objective index to some extent,and effectively improves the clarity of remote sensing images in haze weather conditions.

关 键 词：遥感图像去雾 深度学习 多尺度卷积神经网络 残差学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]