Fast Hyperspectral Image Denoising and Destriping Method Based on Graph Laplacian Regularization
Xin Su, Zhi Zhang, Fang Yang
Abstract
Hyperspectral images (HSIs) contain rich spatial and spectral information about the earth, and are widely used in the remote sensing field. However, an HSI is frequently corrupted by various types of noise, such as Gaussian noise, sparse noise, stripe noise, and so on, which severely limits the subsequent application of the HSI. In this paper, we propose a graph Laplacian regularizer (GLR) to exploit the low-rank information across the bands of the HSI. Compared with the traditional low-rank regularization, our graph Laplacian regularization can achieve equivalent or better performance with less time consumption. Besides, a sparse constraint and a low-rank constraint are employed to remove the sparse and stripe noise. In addition, the augmented Lagrangian multiplier is used to solve each component to restore a clean image. Finally, we have carried out experiments on the simulated and real noisy HSI data. The results show the superiority of the proposed method over state-of-the-art methods, in terms of PSNR and SSIM, time cost and visual effect. The MATLAB code is available through: https://github.com/zzhang-99/FGLR.