基于L1范数优化模型的遥感图像条纹去除方法
作者:
作者单位:

1.中国科学院上海技术物理研究所,上海 200083;2.中国科学院红外探测与成像技术重点实验室,上海 200083;3.中国科学院大学,北京 100049

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中图分类号:

TP751.1

基金项目:

中国科学院上海技术物理研究所创新专项(CX-208)


The method based on L1 norm optimization model for stripe noise removal of remote sensing image
Author:
Affiliation:

1.Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China;2.Key Laboratory of Infrared Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China;3.University of Chinese Academy of Sciences, Beijing 100049, China

Fund Project:

Supported by Innovative Special Foundation of Shanghai Institute of Technical Physics(CX-208)

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    摘要:

    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。为了更好地保护图像的细节信息,在条纹垂直方向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。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,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。

    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 are 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 an 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.

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引用本文

李凯,李文力,韩昌佩.基于L1范数优化模型的遥感图像条纹去除方法[J].红外与毫米波学报,2021,40(2):272~283]. LI Kai, LI Wen-Li, HAN Chang-Pei. The method based on L1 norm optimization model for stripe noise removal of remote sensing image[J]. J. Infrared Millim. Waves,2021,40(2):272~283.]

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  • 收稿日期:2020-04-26
  • 最后修改日期:2021-04-05
  • 录用日期:2020-06-09
  • 在线发布日期: 2021-03-30
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