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Improved morphological component analysis for interference hyperspectral image decomposition

机译:改善干扰高光谱图像分解的形态分量分析

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Due to the special imaging principle, lots of vertical interference stripes exist in the frames of the IHI (interference hyperspectral image) data, which will affect the result of compressed sensing theory or other traditional compression algorithms used on IHI data. In this paper, MCA (morphological component analysis) algorithm is adopted to separate the interference stripes layers and the background layers, and an IMCA (improved MCA) algorithm is proposed according to the characteristics of the IHI data, dictionary learned from the LSMIS (Large Spatially Modulated Interference Spectral Image) data is used to sparsely represent the stripes layers instead of traditional basis, and the condition of iteration convergence is improved. The experimental results prove that the proposed IMCA algorithm can get better results than the traditional MCA, and also can meet the convergence conditions much faster than the traditional MCA. (C) 2015 Elsevier Ltd. All rights reserved.
机译:由于特殊的成像原理,IHI(干扰高光谱图像)数据的帧中存在大量垂直干扰条,这将影响压缩感测理论或在IHI数据上使用的其他传统压缩算法的结果。 在本文中,采用MCA(形态分量分析)算法将干涉条层和背景层分离,并且根据IHI数据的特征提出了一种IMCA(改进的MCA)算法,从LSMIS中学到的字典(大 空间调制的干扰光谱图像)数据用于稀疏地表示条纹层而不是传统的基础,并且提高了迭代会聚的条件。 实验结果证明,所提出的IMCA算法可以比传统的MCA获得更好的结果,并且还可以比传统的MCA更快地满足收敛条件。 (c)2015 Elsevier Ltd.保留所有权利。

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