红外技术2026，Vol.48Issue(1)：70-78,9.
基于非凸低秩张量近似和总变分的高光谱图像去噪
Hyperspectral Image Denoising Based on Non-convex Low-Rank Tensor Approximation and Total Variation
王昕铭 1赵蕊鑫 1申家正 1范露馨1
作者信息
- 1. 马来西亚博特拉大学 工程学院,雪兰莪 沙登 43400
- 折叠
摘要
Abstract
During image acquisition,hyperspectral data are easily contaminated by noise,which affects image quality and reduces the accuracy of subsequent applications.To solve this problem,a hyperspectral image-denoising model based on nonconvex low-rank tensor approximation and total variation is proposed.The nuclear norm of the frame tensor shrinks each singular value equally,which means that the main information of the image cannot be preserved.Therefore,the frame tensor Lγ is proposed to approximate the global low rank of the hyperspectral image and reduce the shrinkage of large singular values to preserve the main information of the image.It is combined with the spatial spectral total variation to fully explore the low-rank characteristics of hyperspectral images while maintaining the local smoothness of the spatial spectra as well as to remove Gaussian and striping noise.An efficient augmented Lagrange multiplier(ALM)algorithm is developed to solve this model.The simulation and real data experiments show that the proposed model outperforms other algorithms in terms of denoising performance and image visual effect,and the contour curve after denoising is not excessively smooth.关键词
高光谱图像/非凸张量近似/框架张量Lγ范数/总变分/图像去噪Key words
hyperspectral image/non-convex tensor approximation/frame tensor Lγ norm/total variation/image denoising分类
信息技术与安全科学引用本文复制引用
王昕铭,赵蕊鑫,申家正,范露馨..基于非凸低秩张量近似和总变分的高光谱图像去噪[J].红外技术,2026,48(1):70-78,9.
