Zhang Qiang, Yuan Qiangqiang, Song Meiping, Yu Haoyang, Zhang Liangpei
IEEE Trans Image Process. 2022;31:6356-6368. doi: 10.1109/TIP.2022.3211471. Epub 2022 Oct 14.
Model-driven methods and data-driven methods have been widely developed for hyperspectral image (HSI) denoising. However, there are pros and cons in both model-driven and data-driven methods. To address this issue, we develop a self-supervised HSI denoising method via integrating model-driven with data-driven strategy. The proposed framework simultaneously cooperates the spectral low-rankness prior and deep spatial prior (SLRP-DSP) for HSI self-supervised denoising. SLRP-DSP introduces the Tucker factorization via orthogonal basis and reduced factor, to capture the global spectral low-rankness prior in HSI. Besides, SLRP-DSP adopts a self-supervised way to learn the deep spatial prior. The proposed method doesn't need a large number of clean HSIs as the label samples. Through the self-supervised learning, SLRP-DSP can adaptively adjust the deep spatial prior from self-spatial information for reduced spatial factor denoising. An alternating iterative optimization framework is developed to exploit the internal low-rankness prior of third-order tensors and the spatial feature extraction capacity of convolutional neural network. Compared with both existing model-driven methods and data-driven methods, experimental results manifest that the proposed SLRP-DSP outperforms on mixed noise removal in different noisy HSIs.
模型驱动方法和数据驱动方法已被广泛用于高光谱图像（HSI）去噪。然而，模型驱动方法和数据驱动方法都有其优缺点。为了解决这个问题，我们通过将模型驱动策略与数据驱动策略相结合，开发了一种自监督HSI去噪方法。所提出的框架同时协作光谱低秩先验和深度空间先验（SLRP-DSP）进行HSI自监督去噪。SLRP-DSP通过正交基和降维因子引入塔克分解，以捕捉HSI中的全局光谱低秩先验。此外，SLRP-DSP采用自监督方式学习深度空间先验。所提出的方法不需要大量干净的HSI作为标签样本。通过自监督学习，SLRP-DSP可以根据自身空间信息自适应调整深度空间先验，以进行降维空间因子去噪。开发了一种交替迭代优化框架，以利用三阶张量的内部低秩先验和卷积神经网络的空间特征提取能力。与现有的模型驱动方法和数据驱动方法相比，实验结果表明，所提出的SLRP-DSP在不同噪声HSI的混合噪声去除方面表现更优。
