基于光谱加权低秩矩阵分解的高光谱影像去噪方法

doi:10.16180/j.cnki.issn1007-7820.2020.05.004

Abstract

Abstract:

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.

Key words: remote sensing, hyperspectral image denoising, spectral weighted matrix, low-rank matrix approximation, weighted nuclear norm, alternating direction method of multipliers

CLC Number:

TP751

LIU Lu,ZHANG Hongyan,ZHANG Liangpei. Hyperspectral Image Denoising via Spectral Weighted Low-rank Matrix Approximation[J].Electronic Science and Technology, 2020, 33(5): 21-27.

Figures/Tables 8

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References 20

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模拟噪声	噪声水平	评价指标	噪声影像	BM4D	LRMR	NonLRMA	本文方法
情况1	Gaussian	MPSNR/dB	22.87	31.51	34.59	34.49	34.98
情况1	Gaussian	MSSIM/%	0.507	0.872	0.954	0.968	0.973
情况2	Gaussian + impulse	MPSNR/dB	14.34	24.30	34.26	34.59	34.85
情况2	Gaussian + impulse	MSSIM/%	0.221	0.638	0.942	0.964	0.968
情况3	Gaussian +stripe+ impulse	MPSNR/dB	13.78	25.01	32.41	33.70	34.13
情况3	Gaussian +stripe+ impulse	MSSIM/%	0.250	0.697	0.926	0.963	0.966

Hyperspectral Image Denoising via Spectral Weighted Low-rank Matrix Approximation

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