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Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties.

Authors :
Liu, Xinxin
Lu, Xiliang
Shen, Huanfeng
Yuan, Qiangqiang
Jiao, Yuling
Zhang, Liangpei
Source :
IEEE Transactions on Geoscience & Remote Sensing. May2016, Vol. 54 Issue 5, p3049-3060. 12p.
Publication Year :
2016

Abstract

Remote sensing images are often contaminated by varying degrees of stripes, which severely affects the visual quality and subsequent application of the data. Unlike with conventional methods, we achieve the destriping by separating the stripe component based on a full analysis of the various stripe properties. Under an optimization framework, an \ell^0-norm-based regularization is used to characterize the global sparse distribution of the stripes. In addition, difference-based constraints are adopted to describe the local smoothness and discontinuity in the along-stripe and across-stripe directions, respectively. The alternating direction method of multipliers is applied to solve and accelerate the model optimization. Experiments with both simulated and real data demonstrate the effectiveness of the proposed model, in terms of both qualitative and quantitative perspectives. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
54
Issue :
5
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
115133590
Full Text :
https://doi.org/10.1109/TGRS.2015.2510418