In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs and the embedded noise, leading to suboptimal performance in practice. In this article, we propose a novel HSI restoration method by fully considering the intrinsic image structures and the complex noise characteristics. For HSIs, the global correlation is captured by the Kronecker-basis-representation-based tensor low-rankness measure, which integrates the insights delivered by both CP and Tucker decompositions; the local regularity is depicted by a plug-and-play spatial-spectral convolutional neural network with strong fitting ability to complex image features. For realistic noise, its statistical characteristics are encoded by a nonidentical and nonindependent distributed mixture of Gaussians distribution with flexible fitting capability. Then, we incorporate these image and noise priors into a probabilistic model based on the maximum a posteriori principle, and develop a solving scheme by combining expectation-maximization and alternating direction method of multipliers. Extensive experimental results on both simulated and real scenarios demonstrate the effectiveness of the proposed method and its superiority over the compared state-of-the- arts.

中文翻译：

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结合固有图像特征与稳健噪声建模的高光谱图像恢复

在高光谱图像 (HSI) 处理中，一个基本问题是从各种退化（例如噪声损坏和信息丢失）中恢复 HSI 数据。然而，大多数现有方法或多或少忽略了关于 HSI 和嵌入噪声的丰富先验知识，导致实践中的性能欠佳。在本文中，我们通过充分考虑固有图像结构和复杂噪声特性，提出了一种新颖的 HSI 恢复方法。对于 HSI，全局相关性由基于 Kronecker-basis-representation-based 张量低秩度量捕获，它集成了 CP 和 Tucker 分解提供的见解；局部规律由即插即用的空间光谱卷积神经网络描述，对复杂图像特征具有很强的拟合能力。对于真实的噪音，它的统计特性由具有灵活拟合能力的高斯分布的非相同和非独立分布式混合编码。然后，我们将这些图像和噪声先验结合到基于最大后验原理的概率模型中，并通过组合期望最大化和乘法器交替方向方法来开发求解方案。在模拟和真实场景上的大量实验结果证明了所提出方法的有效性及其相对于比较现有技术的优越性。并结合期望最大化和乘法器交替方向法开发求解方案。在模拟和真实场景上的大量实验结果证明了所提出方法的有效性及其相对于比较现有技术的优越性。并结合期望最大化和乘法器交替方向法开发求解方案。在模拟和真实场景上的大量实验结果证明了所提出方法的有效性及其相对于比较现有技术的优越性。