基于子空间表示和加权低秩张量正则化的高光谱图像混合噪声去除方法

针对高光谱图像中存在混合噪声的问题, 提出一种基于子空间表示和加权低秩张量正则化的方法去除高光谱图像中的混合噪声. 子空间表示利用光谱频带之间的相关性, 选取合适的正交矩阵, 将高光谱图像投影到低维子空间中, 使提出的算法具有较低的复杂度, 简化去噪过程的同时去除图像中的部分噪声. 去噪过程基于从简化图像中提取的低秩张量进行, 引入加权低秩张量正则化项表征简化图像子空间的先验信息, 基于Tucker分解中核范数的物理意义构建合理的加权机制, 保留高光谱图像的内在结构相关性. 并且设计了一种基于迭代最小化的方法, 用于求解提出的非凸去噪模型. 在模拟和真实数据集上的实验结果表明, 该子空间表示和加权低秩张量正则化方法在定量和定性分析上都取得了较好的去噪效果.

Mixed Noise Removal Method for Hyperspectral Images Based on Subspace Representation and Weighted Low-Rank Tensor Regularization

Aiming at the problem of mixed noise in hyperspectral images, we proposd a method based on subspace representation and weighted low-rank tensor regularization to remove mixed noise from hyperspectral images. The subspace representation used the correlation between spectral bands to select an appropriate orthogonal matrix and project the hyperspectral image into a low-dimensional subspace, so that  the proposed algorithm had lower complexity and simplified the denoising process while  removing part of the noise in the image. The denoising process was based on the low-rank tensor extracted from the simplified image. The regularization term of the weighted low-rank tensor was introduced to represent the prior information of the simplified image subspace. A reasonable weighting mechanism was constructed based on the physical meaning of the nuclear norm in the Tucker decomposition, and the intrinsic structural correlation of the hyperspectral image was preserved. We designed  a method based on  iterative minimization to solve the proposed non-convex denoising model. The experimental results on simulated and real datasets show that the proposed subspace representation and weighted low-rank tensor regularization method achieve high denoising performance in both quantitative and qualitative analysis.