Hyperspectral image (HSI) denoising is a crucial step in enhancing the quality of HSIs. Noise modeling methods can fit noise distributions to generate synthetic HSIs to train denoising networks. However, the noise in captured HSIs is usually complex and difficult to model accurately, which significantly limits the effectiveness of these approaches. In this paper, we propose a multi-stage noise-decoupling framework that decomposes complex noise into explicitly modeled and implicitly modeled components. This decoupling reduces the complexity of noise and enhances the learnability of HSI denoising methods when applied to real paired data. Specifically, for explicitly modeled noise, we utilize an existing noise model to generate paired data for pre-training a denoising network, equipping it with prior knowledge to handle the explicitly modeled noise effectively. For implicitly modeled noise, we introduce a high-frequency wavelet guided network. Leveraging the prior knowledge from the pre-trained module, this network adaptively extracts high-frequency features to target and remove the implicitly modeled noise from real paired HSIs. Furthermore, to effectively eliminate all noise components and mitigate error accumulation across stages, a multi-stage learning strategy, comprising separate pre-training and joint fine-tuning, is employed to optimize the entire framework. Extensive experiments on public and our captured datasets demonstrate that our proposed framework outperforms state-of-the-art methods, effectively handling complex real-world noise and significantly enhancing HSI quality.

翻译：高光谱图像（HSI）去噪是提升HSI质量的关键步骤。噪声建模方法能够拟合噪声分布以生成合成HSI来训练去噪网络。然而，捕获的HSI中的噪声通常复杂且难以精确建模，这显著限制了这些方法的有效性。本文提出一种多阶段噪声解耦框架，将复杂噪声分解为显式建模和隐式建模的组成部分。这种解耦降低了噪声的复杂性，并在应用于真实配对数据时增强了HSI去噪方法的可学习性。具体而言，对于显式建模的噪声，我们利用现有噪声模型生成配对数据以预训练去噪网络，使其具备先验知识以有效处理显式建模的噪声。对于隐式建模的噪声，我们引入一种高频小波引导网络。该网络利用预训练模块的先验知识，自适应提取高频特征以定位并去除真实配对HSI中的隐式建模噪声。此外，为有效消除所有噪声成分并减轻跨阶段的误差累积，采用包含独立预训练和联合微调的多阶段学习策略来优化整个框架。在公开数据集及我们捕获的数据集上进行的大量实验表明，所提出的框架优于现有最先进方法，能有效处理复杂的真实世界噪声并显著提升HSI质量。