基于L1范数优化模型的遥感图像条纹去除方法
投稿时间:2020-04-26  修订日期:2020-06-04  点此下载全文
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作者单位E-mail
李凯 中国科学院上海技术物理研究所 likai_sitp@163.com 
李文力 中国科学院上海技术物理研究所  
韩昌佩 中国科学院上海技术物理研究所 changpei_han@mail.sitp.ac.cn 
基金项目:中国科学院上海技术物理研究所创新专项(CX-208)
中文摘要:在遥感成像系统中,由于非均匀性问题,导致遥感图像中常存在条纹噪声。条纹噪声严重影响了图像质量和后续的应用。与大多数现有的条纹去除方法不同,本文从条纹噪声的结构属性进行分析,通过分离出条纹成分来实现去条纹的目的。在本文优化模型中,基于L1范数的正则化表示条纹的全局稀疏特性;基于差分的约束条件用于描述条纹方向上的平滑度和条纹垂直方向上的不连续性。为了更好的保护图像的细节信息,在条纹垂直方向上的约束上引入了边缘权重因子,最后通过交替方向乘子法(ADMM)对所提模型进行求解和优化。本文用多通道扫描辐射计(AGRI)获取的在轨数据对算法进行了验证并与典型方法进行了比较,结果表明本文所提算法消除条纹噪声的同时更好地保留了细节信息,并且呈现出较好的定性和定量结果。
中文关键词:L1稀疏优化模型,图像去条纹,边缘权重因子,交替方向乘子法,AGRI图像
 
The method based on L1 norm optimization model for stripe noise removal of remote sensing image
Abstract:Stripe noise is a common phenomenon in remote sensing image due to non-uniformity problems. The stripe noise seriously affects the image quality and subsequent applications. Unlike most destriping methods, structural properties of stripe noise is analyzed and the purpose of destriping is achieved by separating the stripe components. In the proposed optimization model, the L0-norm-based is used to describe global sparse property of stripes. In addition, difference-based constraints are adopted to describe the smoothness and discontinuity in the along-stripe and across-stripe directions, respectively. In order to better protect the detailed information of image, an edge weighting factor is introduced in the constraints of across-stripe direction. Finally, the proposed model is solved and optimized by the alternating direction method of multipliers (ADMM). The algorithm is verified by the in-orbit images obtained by Advanced Geosynchronous Radiation Imager (AGRI) in comparison with typical destriping methods. Experimental results show that the proposed algorithm completely eliminates the stripe noise and preserves more details, which shows better qualitative and quantitative result.
keywords:L1 sparse optimization model  image destriping  edge weighting factor  alternating direction method of multipliers  AGRI image
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