Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose an NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is then designed to infer the posterior of the proposed model. As substantiated by our experiments implemented on synthetic and real noisy HSIs, the proposed method performs more robust beyond the state-of-the-arts.

中文翻译：

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使用非独立噪声结构对高光谱图像进行去噪


高光谱图像（HSI）去噪因其对提高HSI质量的重要性而在遥感领域引起了广泛的研究关注。现有的HSI去噪方法主要关注HSI中特定的光谱和空间先验知识，并共享一个共同的基本假设，即HSI中的嵌入噪声是独立同分布（iid）的。然而，在实际场景中，自然 HSI 中存在的噪声总是具有更加复杂的非独立同分布统计结构，并且对这种噪声复杂性的低估往往会明显降低当前方法的鲁棒性。为了缓解这个问题，本文首次尝试使用非独立同分布高斯混合（NMoGs）噪声假设来对 HSI 噪声进行建模，该假设非常符合自然 HSI 所具有的噪声特性，因此能够适应各种实际情况。噪声形状。然后，我们将这种噪声建模策略集成到低秩矩阵分解（LRMF）模型中，并在贝叶斯框架中提出了 NMoG-LRMF 模型。然后设计变分贝叶斯算法来推断所提出模型的后验。正如我们在合成和真实噪声 HSI 上实施的实验所证实的那样，所提出的方法比现有技术更加稳健。