Hyperspectral Image Denoising with Log-Based Robust PCA

• URL: http://arxiv.org/abs/2105.11927v1
• Date: Tue, 25 May 2021 13:32:01 GMT
• Title: Hyperspectral Image Denoising with Log-Based Robust PCA
• Authors: Yang Liu, Qian Zhang, Yongyong Chen, Qiang Cheng and Chong Peng
• Abstract summary: It is a challenging task to remove heavy and mixed types noise from Hyperspectral images (HSIs) We propose a novel non approximation approach to RPCA for HSI denoising. Experiments on both simulated real HSIs demonstrate the effectiveness of the proposed method.
• Score: 29.566894890976194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising, which adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the low-rank or column-wise sparse properties for the component matrices, respectively.For the $\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator, which can be generally used in other problems. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs.

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