Total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising

Zhaojun Wu; Qiang Wang; Zhenghua Wu; Yi Shen

doi:10.1117/1.JEI.25.1.013037

26 February 2016 Total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising

Zhaojun Wu, Qiang Wang, Zhenghua Wu, Yi Shen

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 1, 013037 (February 2016). https://doi.org/10.1117/1.JEI.25.1.013037

Abstract

Many nuclear norm minimization (NNM)-based methods have been proposed for hyperspectral image (HSI) mixed denoising due to the low-rank (LR) characteristics of clean HSI. However, the NNM-based methods regularize each eigenvalue equally, which is unsuitable for the denoising problem, where each eigenvalue stands for special physical meaning and should be regularized differently. However, the NNM-based methods only exploit the high spectral correlation, while ignoring the local structure of HSI and resulting in spatial distortions. To address these problems, a total variation (TV)-regularized weighted nuclear norm minimization (TWNNM) method is proposed. To obtain the desired denoising performance, two issues are included. First, to exploit the high spectral correlation, the HSI is restricted to be LR, and different eigenvalues are minimized with different weights based on the WNNM. Second, to preserve the local structure of HSI, the TV regularization is incorporated, and the alternating direction method of multipliers is used to solve the resulting optimization problem. Both simulated and real data experiments demonstrate that the proposed TWNNM approach produces superior denoising results for the mixed noise case in comparison with several state-of-the-art denoising methods.

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Zhaojun Wu, Qiang Wang, Zhenghua Wu, and Yi Shen "Total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising," Journal of Electronic Imaging 25(1), 013037 (26 February 2016). https://doi.org/10.1117/1.JEI.25.1.013037

Published: 26 February 2016

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