Recently, the low-rank property of different components extracted from the image has been considered in man hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial self-similarity (NSS) and global spectral correlation (GSC), which break the intrinsic structure correlation of hyperspectral image (HSI) and thus lead to poor restoration quality. In addition, most of them suffer from heavy computational burden issues due to the involvement of singular value decomposition operation on matrix and tensor in the original high-dimensionality space of HSI. We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Specifically, to employ the GSC among spectral bands, the noisy HSI is projected into a low-dimensional subspace which simplified calculation. After that, a weighted low-rank tensor regularization term is introduced to characterize the priors in the reduced image subspace. Moreover, we design an algorithm based on alternating minimization to solve the nonconvex problem. Experiments on simulated and real datasets demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.

翻译：最近,从图像中提取的不同部件的低位属性在人造超光谱图像分解方法中得到了考虑。然而,这些方法通常会将3D 发压至 2D 矩阵或1D 矢量器用于利用先前的信息,例如非局部空间自异性(NSS)和全球光谱相关(GSC),这些信息打破了超光谱图像的内在结构相关性,从而导致恢复质量低下。此外,它们中的大多数都由于在HSI原高维空间的矩阵和振幅上采用单值分解操作,而面临沉重的计算负担问题。我们使用子空间代表制和加权低位阵列调节(SWLRTR)作为模型,以消除超光谱图像中混合的噪音。具体地说,为了在光谱波段中使用GSC,热度的热度预测会进入一个低维度的子空间,从而简化了计算质量。此外,还引入了一个加权的低位数调调调调调调调调术语,以描述图像子空间中先前的特性。此外,我们设计了一种算法,其基础是把最小化的最小化改为解决非conxSWMLR的问题,而不是在模拟上进行更精确的实验。