The method based on L1 norm optimization model for stripe noise removal of remote sensing image
投稿时间:2020-04-26  修订日期:2020-06-04  download
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李凯 中国科学院上海技术物理研究所 
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 
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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Copyright:《Journal of Infrared And Millimeter Waves》