西安电子科技大学学报（自然科学版）2024，Vol.51Issue(2)：157-169,13.DOI:10.19665/j.issn1001-2400.20230412
张量分解和自适应图全变分的高光谱图像去噪
Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation
摘要
Abstract
During the acquisition process of hyperspectral images,various noises are inevitably introduced due to the influence of objective factors such as observation conditions,material properties of the imager,and transmission conditions,which severely reduces the quality of hyperspectral images and limits the accuracy of subsequent processing.Therefore,denoising of hyperspectral images is an extremely important preprocessing step.For the hyperspectral image denoising problem,a denoising algorithm,which is based on low-rank tensor decomposition and adaptive weight graph total variation regularization named LRTDGTV,is proposed in this paper.Specifically,Low-rank tensor decomposition is used to characterize the global correlation among all bands,and adaptive weight graph total variation regularization is adopted to characterize piecewise smoothness property of hyperspectral images in the spatial domain and preserve the edge information of hyperspectral images.In addition,sparse noise,including stripe noise,impulse noise and deadline noise,and Gaussian noise are characterized by l1-norm and Frobenius-norm,respectively.Thus,the denoising problem can be formulated into a constrained optimization problem involving low-rank tensor decomposition and adaptive weight graph total variation regularization,which can be solved by employing the augmented Lagrange multiplier(ALM)method.Experimental results show that the proposed hyperspectral image denoising algorithm can fully characterize the inherent structural characteristics of hyperspectral images data and has a better denoising performance than the existing algorithms.关键词
高光谱图像去噪/Tucker分解/自适应图全变分Key words
hyperspectral image denoising/tucker decomposition/adaptive weight graph total variation分类
信息技术与安全科学引用本文复制引用
蔡明娇,蒋俊正,蔡万源,周芳..张量分解和自适应图全变分的高光谱图像去噪[J].西安电子科技大学学报（自然科学版）,2024,51(2):157-169,13.基金项目
国家自然科学基金(62171146,62261014) （62171146,62261014）
广西创新驱动发展专项(桂科AA21077008) （桂科AA21077008）
广西自然科学杰出青年基金(2021GXNSFFA220004) （2021GXNSFFA220004）
广西科技基地和人才专项(桂科AD21220112) （桂科AD21220112）
广西无线宽带通信与信号处理重点实验室主任基金(GXKL06220107) （GXKL06220107）
桂林电子科技大学研究生教育创新计划(2022YCXS039) （2022YCXS039）
