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作 者:卜丽静[1] 李秀伟 张正鹏[1] 姜昊男 BU Lijing;LI Xiuwei;ZHANG Zhengpeng;JIANG Haonan(School of Surveying and Mapping and Geographical Sciences,Liaoning University of Engineering and Technology,Fuxin 123000,China)
机构地区:[1]辽宁工程技术大学测绘与地理科学学院,阜新123000
出 处:《国土资源遥感》2020年第1期27-34,共8页Remote Sensing for Land & Resources
基 金:国家自然科学基金项目“凝视卫星视频运动场景超分重建”(编号:41501504)资助。
摘 要:对于遥感图像中降质模糊的问题,经典的图像复原方法由于模糊函数难以估计等原因,复原效果较差。为了避免估计模糊函数带来的困难,通过深度学习的方法对图像进行去模糊,研究了基于条件生成对抗网络(conditional generative adversarial nets,CGAN)的图像复原方法。首先创建训练网络的训练库,然后设置网络训练的初始参数,该网络以对抗的方式来使生成模型和判别模型进行交替学习,通过不断学习降质图像和清晰图像之间的差异,并结合了对抗损失和感知损失来缩小两者之间的差异,实现图像复原。实验采用以GOPRO数据集为基础的混合模糊训练库来训练网络,并与其他方法进行了对比试验,结果表明,在图像细节和评价指标方面,CGAN具有较好的复原效果,保证了复原图像的细节信息和纹理信息,证明了该方法可以用于遥感图像的复原。For the problem of degrading and blurring in remote sensing images,the classical image restoration methods have poor restoration effect due to the difficulty of estimating the blur function.In order to avoid the difficulty of estimating the blur function,the authors have studied the image restoration method based on Conditional Generative Adversarial Nets(CGAN)through depth learning.Firstly,the training database of the training network is created,and then the initial parameters of the training network are set.The network alternately learns the generator model and the discriminator model in the adversarial way.By learning the difference between the degraded image and the clear image continuously and combining the adversarial loss with the perceptual loss,the difference between them can be reduced and the image restoration can be realized.A Hybrid blur training library based on GOPRO data set is used to train the network,and is compared with other methods.The results show that this means has better restoration effect in image details and evaluation indexes.The details and texture information of the restored image are guaranteed,and the method of conditional generation antagonism network is proved to be applicable to the restoration of remote sensing image.
分 类 号:TP79[自动化与计算机技术—检测技术与自动化装置]
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