Hyperspectral image compressive sensing reconstruction (HSI-CSR) can largely reduce the high expense and low efficiency of transmitting HSI to ground stations by storing a few compressive measurements, but how to precisely reconstruct the HSI from a few compressive measurements is a challenging issue. It has been proven that considering the global spectral correlation, spatial structure, and nonlocal self-similarity priors of HSI can achieve satisfactory reconstruction performances. However, most of the existing methods cannot simultaneously capture the mentioned priors and directly design the regularization term to the HSI. In this article, we propose a novel subspace-based nonlocal tensor ring decomposition method (SNLTR) for HSI-CSR. Instead of designing the regularization of the low-rank approximation to the HSI, we assume that the HSI lies in a low-dimensional subspace. Moreover, to explore the nonlocal self-similarity and preserve the spatial structure of HSI, we introduce a nonlocal tensor ring decomposition strategy to constrain the related coefficient image, which can decrease the computational cost compared to the methods that directly employ the nonlocal regularization to HSI. Finally, a well-known alternating minimization method is designed to efficiently solve the proposed SNLTR. Extensive experimental results demonstrate that our SNLTR method can significantly outperform existing approaches for HSI-CSR.

中文翻译：

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基于子空间的非局部张量环分解的高光谱图像压缩感知重建

高光谱图像压缩感测重建（HSI-CSR）通过存储一些压缩测量值可以大大减少将HSI传输到地面站的高成本和低效率，但是如何从一些压缩测量值中精确地重建HSI是一个具有挑战性的问题。已经证明，考虑到HSI的全局频谱相关性，空间结构和非局部自相似性，可以实现令人满意的重建性能。但是，大多数现有方法无法同时捕获所提到的先验条件，也不能直接为HSI设计正则项。在本文中，我们提出了一种用于HSI-CSR的新颖的基于子空间的非局部张量环分解方法（SNLTR）。与其设计针对HSI的低秩逼近的正则化，我们假设HSI位于低维子空间中。此外，为了探索非局部自相似性并保留HSI的空间结构，我们引入了一种非局部张量环分解策略来约束相关系数图像，与直接将非局部正则化方法应用于HSI的方法相比，可以降低计算成本。最后，设计了一种众所周知的交替最小化方法来有效解决所提出的SNLTR。大量的实验结果表明，我们的SNLTR方法可以大大优于HSI-CSR的现有方法。与直接将非局部正则化应用于HSI的方法相比，这可以减少计算成本。最后，设计了一种众所周知的交替最小化方法来有效解决所提出的SNLTR。大量的实验结果表明，我们的SNLTR方法可以大大优于HSI-CSR的现有方法。与直接将非局部正则化应用于HSI的方法相比，这可以减少计算成本。最后，设计了一种众所周知的交替最小化方法来有效解决所提出的SNLTR。大量的实验结果表明，我们的SNLTR方法可以大大优于HSI-CSR的现有方法。