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Panchromatic and multi-spectral image fusion method based on two-step sparse representation and wavelet transform

机译:基于两步稀疏表示和小波变换的全光谱和多光谱图像融合方法

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Based on the characteristics of two-step sparse coding and multi-scale analysis of wavelet transform, a novel fusion algorithm based on two-step sparse coding (Two Step Sparse Representation, TSSR) and wavelet transform is proposed. The two-step sparse strategy is used to construct the corresponding dictionary for the low-frequency component and the down- sampled low-frequency component respectively, which avoids the training process of the traditional sparse representation and improves the computing speed. At the same time, the sparse coefficient solution based on two-step sparse coding is closer to the original signal than the one-step sparse solution in traditional sparse representation, and the precision of the algorithm is higher. Experimental results and analysis show that the proposed method can not only keep the spectral characteristics, but also can effectively integrate the spatial detail information of panchromatic images. The computing time is much faster than the traditional sparse method, and it has more advantages than wavelet transform and traditional sparse representation with excellent fusion effect.
机译:基于两步稀疏编码的特点和小波变换的多尺度分析,提出了一种基于两步稀疏编码(两步稀疏表示,TSSR)和小波变换的新型融合算法。两步稀疏策略用于分别构造低频分量和下采样的低频分量的相应词典,其避免了传统稀疏表示的训练过程并提高了计算速度。同时,基于两步稀疏编码的稀疏系数解决方案比传统稀疏表示中的一步稀疏解决方案更靠近原始信号,并且算法的精度更高。实验结果和分析表明,该方法不仅可以保持光谱特性,还可以有效地集成了全色图像的空间细节信息。计算时间比传统的稀疏方法快得多,并且它具有比小波变换和具有出色融合效果的传统稀疏表示的优点。

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