Abstract Hyperspectral image (HSI) denoising is a prevalent research topic in the remote sensing area. In general, HSIs are inevitably impaired by different types of noise during the data acquisition. To fully characterize the underlying structures of clean HSI and remove mixed noises, we introduce a novel HSI denoising method named total variation-regularized bilinear factorization (BFTV) model. Specifically, we first utilize the bilinear factorization term to explore the globally low-rank structure of the clean HSI and suppress a certain degree of Gaussian noise, so as to make BFTV free to the singular value decomposition. Then the l1-norm is applied to detect and separate the mixed sparse noise including impulse noise, deadlines, and stripes. Besides, the TV regularization is introduced to describe the locally piecewise smoothness property of the clean HSI both in spatial and spectral domains. To solve this optimization problem, we devise an effective algorithm based on the augmented Lagrange multiplier method. Numerical experiments on five different kinds of mixed noise scenarios and one real world data have tested and demonstrated the superior denoising power of the proposed BFTV model compared with three state-of-the-art low-rank-based approaches.

中文翻译：

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基于全变正正则化双线性分解的高光谱图像去噪

摘要 高光谱图像（HSI）去噪是遥感领域的热门研究课题。通常，在数据采集过程中，HSI 不可避免地会受到不同类型噪声的影响。为了充分表征干净 HSI 的底层结构并去除混合噪声，我们引入了一种新的 HSI 去噪方法，称为全变正正则化双线性分解 (BFTV) 模型。具体来说，我们首先利用双线性分解项来探索干净 HSI 的全局低秩结构并抑制一定程度的高斯噪声，从而使 BFTV 自由进行奇异值分解。然后应用 l1 范数来检测和分离混合稀疏噪声，包括脉冲噪声、截止时间和条纹。除了，引入 TV 正则化来描述干净 HSI 在空间域和谱域中的局部分段平滑特性。为了解决这个优化问题，我们设计了一种基于增广拉格朗日乘子法的有效算法。对五种不同类型的混合噪声场景和一种真实世界数据的数值实验已经测试并证明了所提出的 BFTV 模型与三种最先进的基于低秩的方法相比具有优越的去噪能力。