张量组稀疏表示的高光谱图像去噪算法

摘要

A novel algorithm for hyperspectral image (HSI)denoising is proposed based on tensor group sparse representation.A HSI is considering as 3 order tensor.First,a HSI is divided into small tensor blocks.Second,similar blocks are gathered into clusters,and then a tensor group sparse representation model is constructed based on every cluster.Through exploiting HSI spectral correlation and nonlocal similarity over space,the model constrained tensor group sparse representation can be decomposed into a series of unconstrained low-rank tensor approximation problems,which can be solved using the tensor decomposition technique.The experiment results on the synthetic and realhyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.%提出了一种基于张量组稀疏表示的高光谱遥感影像降噪.高光谱影像数据可视为三阶张量.首先,高光谱图像被划分为小的张量分块,然后,对相似的张量分块进行聚类,并对聚类分组进行稀疏表示.基于高光谱图像的空间非局部自相似性和光谱相关性,将张量组稀疏表示模型分解为一系列无约束低秩张量的近似问题,进而通过张量分解进行求解.对模拟和真实高光谱数据进行试验,验证了该算法的有效性.