基于光谱加权低秩矩阵分解的高光谱影像去噪方法

高光谱遥感影像在获取和传输过程中会受到各种类型噪声的污染,不仅降低影像质量,也限制了其后续应用的精度。高光谱影像噪声类型复杂多样,且噪声在不同波段上的强度也并不相同。通过引入光谱域上的权重矩阵,文中提出了一种基于光谱加权低秩矩阵分解的高光谱遥感影像混合噪声去除方法,利用光谱权重矩阵均衡不同波段的噪声强度差异性。为进一步将噪声与纯净影像分离,利用加权核范数最小化来约束纯净高光谱影像的局部低秩结构,并利用交替方向乘子法对所提出的模型进行优化求解。通过对模拟与真实高光谱遥感数据的实验,验证了所提方法的有效性与优越性。

Hyperspectral Image Denoising via Spectral Weighted Low-rank Matrix Approximation

HSIs are often contaminated by various types of noise, which degrades the quality of the acquired image and limits the subsequent application. Furthermore, the noise of HSI appears different statics and intensity in different bands. In this paper, a spectral weighted low-rank approximation model was proposed for hyperspectral image denoising. The spectral weighted matrix was introduced to balance the data fidelity of the different bands in consideration of their different noise intensity. To further separate the noise from the clean image, weighted nuclear norm minimization was utilized to depict the patch-wise low-rank structure of the high dimensional HSI. The proposed model was formulated into a linear equality-constrained problem and solved by alternating direction method of multipliers. Experimental results on both simulated and real HSI datasets validated the effectiveness and superiority of the proposed method.