Hyperspectral imagery (HSI) restoration is a fundamental problem as a preprocessing step. In this letter, we present a novel auto-weighted nonlocal tensor ring rank minimization (ANTRRM) to reduce noise in HSI. First, nonlocal cuboid tensorization (NCT), built by similar grouping cuboids in HSI data, exploits the nonlocal self-similarity and the spatial–spectral correlation simultaneously. Then, the proposed model introduces nuclear norm (NN) regularization via nonlocal tensor ring with mode-{ $d$ , $l$ } unfolding. An auto-weighted optimization is employed to represent the different importance of TR unfolding. Finally, the alternating direction method of multipliers (ADMM) scheme is employed to solve the proposed model efficiently. Experiments on two simulation HSIs datasets and a real HSI dataset were carried out, compared with representative approaches in visual and quantitative comparison. The proposed ANTRRM method is superior except in a few cases.

中文翻译：

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通过自动加权非局部张量环秩最小化的高光谱图像恢复

高光谱图像 (HSI) 恢复是作为预处理步骤的基本问题。在这封信中，我们提出了一种新颖的自动加权非局部张量环秩最小化 (ANTRRM)，以减少 HSI 中的噪声。首先，由 HSI 数据中相似的长方体分组构建的非局部长方体张量化 (NCT) 同时利用了非局部自相似性和空间-光谱相关性。然后，所提出的模型通过非局部张量环引入核范数（NN）正则化，模式为-{ $d$ , $l$ } 展开。采用自动加权优化来表示 TR 展开的不同重要性。最后，采用交替方向乘法器（ADMM）方案来有效地求解所提出的模型。对两个模拟 HSI 数据集和一个真实 HSI 数据集进行了实验，并与视觉和定量比较中的代表性方法进行了比较。除了少数情况外，所提出的 ANTRRM 方法更胜一筹。