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本文基于压缩感知理论,利用高光谱图像谱间存在极强的相关性,提出一种基于谱间和帧内协同稀疏的高光谱图像压缩感知模型(hyperspectral image collaborative sparsity measure,HICoSM).模型包括对高光谱图像各波段帧内及其帧间3方面的稀疏性挖掘:一是对各波段帧基于局部区域平滑性的稀疏性度量;二是对各波段帧基于非局部区域纹理、边缘等细节信息的自相似性稀疏性度量;三是相邻波段帧基于谱间相关性的预测稀疏性度量,具体利用前一个波段帧,通过最小二乘法线性预测形成当前波段帧的预测帧,通过确定预测帧与当前波段帧的最佳预测差实现谱间的稀疏性度量.进一步,给出了所提出模型的数值计算过程.仿真实验表明,模型HICoSM在对各个波段帧的稀疏性进行度量的基础上,通过挖掘和测量高光谱图像波段间的谱间稀疏性,有效地提高了各波段帧在压缩感知恢复阶段的解码质量.
In this paper, based on the compressed sensing theory, a hyperspectral image collaborative sparsity measure (HICoSM) based on the inter-spectral and intra-frame co-sparseness is proposed by using the strong correlation between the hyperspectral image spectra. Hyperspectral image in each band and its inter-frame 3 sparsity mining: one for each band frame based on local area smoothness sparseness measurement; the second is for each band frame based on non-local texture, edge and other details of the information The third one is the prediction sparsity measure of the adjacent band based on the correlation between the spectrums, and the prediction of the current band frame is formed by the least squares linear prediction using the previous band frame. By determining the prediction frame, And the best prediction difference of the current band frame to achieve the sparsity between spectra.Furthermore, the numerical calculation process of the proposed model is given.The simulation results show that the model HICoSM, based on the measurement of the sparsity of each band, By digging and measuring sparsity between spectrum bands of hyperspectral images, the frame of each band is effectively improved in the phase of compressive sensing recovery Code quality.