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Pansharpening of multispectral images using the nonseparable framelet lifting transform with high vanishing moments

机译:使用具有高消失矩的不可分小框架提升变换对多光谱图像进行全景锐化

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摘要

This paper proposes a novel nonseparable lifting scheme for wavelet frames with high vanishing moments. A specific nonseparable framelet lifting transform (NFLT), combined with a modified covariance intersection (CI) algorithm, has been applied to pansharpening of multispectral images. Experiments are carried out on the multispectral and panchromatic images acquired by the SPOT, QuickBird and Landsat spaceborne sensors. Benefiting from the high order of vanishing moments, the proposed NFLT can distinguish the low- and high-frequency efficiently and can compact most of the energy into the low-pass subband. Thus the spectral distortion can be minimized. Experimental results show that the NFLT-CI method reduces the spectral distortion while improves the spatial resolution simultaneously, and outperforms the other state-of-the-art methods derived from various transforms and injection models.
机译:针对高消失力矩的小波框架,提出了一种新的不可分离的提升方案。特定的不可分离的小框架提升变换(NFLT)与改进的协方差相交(CI)算法相结合,已应用于多光谱图像的全景锐化。对由SPOT,QuickBird和Landsat星载传感器获取的多光谱和全色图像进行了实验。受益于消失力矩的高阶,提出的NFLT可以有效地区分低频和高频,并将大部分能量压缩到低通子带中。因此,可以使频谱失真最小化。实验结果表明,NFLT-CI方法可减少光谱失真,同时提高空间分辨率,并且优于其他从各种变换和注入模型获得的最新方法。

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