Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex degradation, due to the discrepancy between synthetic training data and real data. Although some unsupervised denoisers have been developed to learn priors on real data, the noise assumptions or image priors in these methods limit their performances. In this paper, an unsupervised noise estimation and restoration (UNER) framework is proposed based on disentangled representation learning, to create an interband representation space that is resistant to noise within a single HSI, i.e., an interband-invariant representation space. Real HSIs are categorized internally into noisy and clean bands by noise intensity estimation. Intraband and interband disentangled reconstruction are designed to train two encoder-decoder modules to learn the interband-invariant representation. Noise patterns are separated from HSIs by transforming noisy bands into the representation space without introducing hand-crafted priors. The estimated noise and clean bands are then combined to train the self-supervised interband information restoration module, thereby exploiting spectral correlation and restoring the latent noise-free data with high information fidelity. In this way, the UNER framework can learn specific priors from real HSIs without clean data and be applied to various scenarios with complicated noise patterns. Noise removal experiments conducted on three airborne hyperspectral datasets representing urban, agricultural, and forestry areas respectively demonstrate the superiority of the proposed UNER framework over the state-of-the-art hyperspectral denoising methods.

中文翻译：

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基于带间不变表示学习的无监督高光谱噪声估计和恢复


从不同成像平台获取的高光谱图像 （HSI） 不可避免地受到多种噪声的污染。然而，由于合成训练数据与真实数据之间的差异，现有的基于监督学习的降噪方法在复杂退化的数据上往往表现出较差的泛化性。尽管已经开发了一些无监督降噪器来学习真实数据的先验，但这些方法中的噪声假设或图像先验限制了它们的性能。在本文中，提出了一种基于解纠缠表示学习的无监督噪声估计和恢复 （UNER） 框架，以在单个 HSI 内创建一个抗噪声的带间表示空间，即带间不变表示空间。通过噪声强度估计，实际 HSI 在内部分为噪声频段和干净频段。旨在训练两个编码器-解码器模块来学习带间不变表示的带内和带间解纠缠重建。通过将噪声波段转换为表示空间，无需引入手工制作的先验，即可将噪声模式与 HSI 分离。然后将估计的噪声和干净频带结合起来，训练自监督的频带间信息恢复模块，从而利用频谱相关性，以高信息保真度恢复潜在的无噪声数据。通过这种方式，UNER 框架可以从没有干净数据的真实 HSI 中学习特定的先验，并应用于具有复杂噪声模式的各种场景。 在分别代表城市、农业和林业地区的三个机载高光谱数据集上进行的噪声去除实验表明，所提出的 UNER 框架优于最先进的高光谱去噪方法。