A remote sensing image destriping model based on low-rank and directional sparse constraint

doi:10.3390/rs13245126

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	A remote sensing image destriping model based on low-rank and directional sparse constraint
	X. Wu; H. Qu; L. Zheng; T. Gao and Z. Zhang
	2021
发表期刊	Remote Sensing
ISSN	20724292
卷号	13 期号:24
摘要	Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this paper a new model, based on total variation (TV) regularization, global low rank and directional sparsity constraints, is proposed for the removal of vertical stripes. TV regularization is used to preserve details, and the global low rank and directional sparsity are used to constrain stripe noise. The directional and structural characteristics of stripe noise are fully utilized to achieve a better removal effect. Moreover, we designed an alternating minimization scheme to obtain the optimal solution. Simulation and actual experimental data show that the proposed model has strong robustness and is superior to existing competitive destriping models, both subjectively and objectively. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/rs13245126
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收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65584
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	X. Wu,H. Qu,L. Zheng,et al. A remote sensing image destriping model based on low-rank and directional sparse constraint[J]. Remote Sensing,2021,13(24).
APA	X. Wu,H. Qu,L. Zheng,&T. Gao and Z. Zhang.(2021).A remote sensing image destriping model based on low-rank and directional sparse constraint.Remote Sensing,13(24).
MLA	X. Wu,et al."A remote sensing image destriping model based on low-rank and directional sparse constraint".Remote Sensing 13.24(2021).

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