张量分解和自适应图全变分的高光谱图像去噪

doi:10.19665/j.issn1001-2400.20230412

Abstract

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.

Key words: hyperspectral image denoising, tucker decomposition, adaptive weight graph total variation

CLC Number:

TP751

CAI Mingjiao, JIANG Junzheng, CAI Wanyuan, ZHOU Fang. Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation[J].Journal of Xidian University, 2024, 51(2): 157-169.

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References 22

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Indian Pines(145×145×224)
Case	Level	Indexes	LRTV	LRTDTV	GLF	NGmeet	FGSLR	LRTDGTV
1	G=0.075	R_MPSNR	39.493 4	39.248 4	28.838 5	28.235 1	37.563 9	40.068 5
	P=0.15	R_MSSIM	0.984 5	0.990 9	0.959 6	0.963 0	0.964 0	0.993 5
		R_ERGAS	38.888 1	27.560 6	88.330 8	95.288 5	32.521 7	25.000 7
2	G=0.1	R_MPSNR	37.393 6	36.739 5	26.410 3	26.399 3	35.486 9	38.807 6
	P=0.2	R_MSSIM	0.975 6	0.984 1	0.940 6	0.935 6	0.936 8	0.991 0
		R_ERGAS	58.548 6	36.399 2	117.352 9	117.413 1	40.851 4	28.607 9
3	Gaussian	R_MPSNR	36.622 8	37.306 8	31.273 3	29.419 4	36.006 0	38.741 2
	+impulse	R_MSSIM	0.973 4	0.985 9	0.969 6	0.965 5	0.949 2	0.991 3
		R_ERGAS	72.491 7	34.760 7	77.143 5	88.708 0	38.753 4	28.823 4
4	Gaussian	R_MPSNR	36.416 1	37.143 7	29.603 4	28.633 6	35.701 0	38.451 6
	+impulse	R_MSSIM	0.973 2	0.986 1	0.908 6	0.960 6	0.948 0	0.991 0
	+deadlines	R_ERGAS	78.409 4	35.045 4	96.641 6	97.248 9	40.135 0	29.614 0

Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation

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