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A Universal Destriping Framework Combining 1-D and 2-D Variational Optimization Methods
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DOI：10.1109/TGRS.2017.2755016.png)
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摘要
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Striping effects are a common phenomenon in remote-sensing imaging systems, and they can exhibit considerable differences between different sensors. Such artifacts can greatly degrade the quality of the measured data and further limit the subsequent applications in higher level remotesensing products. Although a lot of destriping methods have been proposed to date, a few of them are robust to different types of stripes. In this paper, we conduct a thorough feature analysis of stripe noise from a novel perspective. With regard to the problem of striping diversity and complexity, we propose a universal destriping framework. In the proposed destriping procedure, a 1-D variational method is first designed and utilized to estimate the statistical feature-based guidance. The guidance information is then incorporated into 2-D optimization to control the image estimation for a reliable and clean output. The iteratively reweighted least-squares method and alternating direction method of multipliers are exploited in the proposed approach to solve the minimization problems. Experiments under various cases of simulated and real stripes confirm the effectiveness and robustness of the proposed model in terms of the qualitative and quantitative comparisons with other approaches.
Keyword:
Destriping
mean cross-track profile
remote-sensing image
universal framework
variational optimization
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IF:
8.6
论文数:
2.1W
被引数:
10.7W
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Simultaneous Destriping and Denoising for Remote Sensing Images With Unidirectional Total Variation and Sparse Representation基于单向全变分和稀疏表示的遥感图像同时去噪和去噪
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