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Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation
Zhou, D. B.; D. J. Wang; L. J. Huo and P. Jia
2016
发表期刊Journal of the Optical Society of Korea
卷号20期号:6
摘要Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l(1)-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.
文章类型期刊
收录类别SCI ; EI
语种英语
文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/57488
专题中科院长春光机所知识产出
推荐引用方式
GB/T 7714
Zhou, D. B.,D. J. Wang,L. J. Huo and P. Jia. Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation[J]. Journal of the Optical Society of Korea,2016,20(6).
APA Zhou, D. B.,D. J. Wang,&L. J. Huo and P. Jia.(2016).Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation.Journal of the Optical Society of Korea,20(6).
MLA Zhou, D. B.,et al."Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation".Journal of the Optical Society of Korea 20.6(2016).
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