Hyperspectral image (HSI) denoising is an essential preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for HSI denoising tasks. In recent years, denoising methods based on low-rank subspaces have garnered attention. Within the low-rank decomposition framework (LRDF), the restoration of HSIs can be formulated as a problem of restoring two subspace factors. Since the rank of the HSI data has been predetermined by LRDF, subspace-based methods have already characterized the spectral low-rankness information. Next, subspace-based methods only need to encode spatial priors for HSIs. Existing subspace-based methods either rely on a manual-designed regularization or a pre-trained deep neural network. The former fails to fully capture the intrinsic priors of the HSI, while the latter may encounter generalization issues. Inspired by the unsupervised deep image prior (DIP) technique, this article proposes a double subspace deep prior (DSDP) model to track the mentioned issues. In this model, the two subspace factors are parallelly represented by two deep neural networks. By incorporating popular attention modules into classical convolutional neural networks, the well-designed subspace factor neural network can effectively capture the deep prior of the two subspace factors separately from each HSI in an unsupervised manner. Additionally, the total variation (TV) regularizer is added to constrain the generation of the subspace factor neural network, and further to ensure the effectiveness and robustness of the parameter learning process. Extensive experiments demonstrate that our method outperforms a series of competing methods.

中文翻译：

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通过双子空间深度先验进行高光谱图像去噪


高光谱图像 (HSI) 去噪是下游应用的重要预处理步骤。充分表征 HSI 的空间谱先验对于 HSI 去噪任务至关重要。近年来，基于低秩子空间的去噪方法引起了人们的关注。在低秩分解框架（LRDF）内，HSI 的恢复可以表示为恢复两个子空间因子的问题。由于HSI数据的秩已经由LRDF预先确定，基于子空间的方法已经表征了谱低秩信息。接下来，基于子空间的方法只需要对 HSI 的空间先验进行编码。现有的基于子空间的方法要么依赖于手动设计的正则化，要么依赖于预先训练的深度神经网络。前者无法完全捕捉恒生指数的内在先验，而后者可能会遇到泛化问题。受无监督深度图像先验（DIP）技术的启发，本文提出了一种双子空间深度先验（DSDP）模型来跟踪上述问题。在该模型中，两个子空间因子由两个深度神经网络并行表示。通过将流行的注意力模块合并到经典的卷积神经网络中，精心设计的子空间因子神经网络可以以无监督的方式有效地捕获来自每个 HSI 的两个子空间因子的深层先验。此外，还添加了全变分（TV）正则器来约束子空间因子神经网络的生成，进一步保证参数学习过程的有效性和鲁棒性。大量的实验表明，我们的方法优于一系列竞争方法。