scholarly journals A Probabilistic Hyperspectral Imagery Restoration Method

2019 ◽  
Vol 9 (12) ◽  
pp. 2529
Author(s):  
Wei Wei ◽  
Jiatao Nie ◽  
Chunna Tian
Keyword(s):  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Low Rank ◽  
Rank Number ◽  
Rank Matrix ◽  
Restoration Method ◽  
Imagery Processing ◽  
Low Rank Matrix ◽  
Mixture Of Gaussian ◽  
Restoration Problem

Hyperspectral image (HSI) restoration is an important task of hyperspectral imagery processing, which aims to improve the performance of the subsequent HSI interpretation and applications. Considering HSI is always influenced by multiple factors—such as Gaussian noise, stripes, dead pixels, etc.—we propose an HSI-oriented probabilistic low-rank restoration method to address this problem. Specifically, we treat the expected clean HSI as a low-rank matrix. We assume the distribution of complex noise obeys a mixture of Gaussian distributions. Then, the HSI restoration problem is casted into solving the clean HSI from its counterpart with complex noise. In addition, considering the rank number need to be assigned manually for existing low-rank based HSI restoration method, we propose to automatically determine the rank number of the low-rank matrix by taking advantage of hyperspectral unmixing. Experimental results demonstrate HSI image can be well restored with the proposed method.

Hyperspectral Image Restoration Using Low-Rank Matrix Recovery

2014 ◽  
Vol 52 (8) ◽  
pp. 4729-4743 ◽  
Author(s):  
Hongyan Zhang ◽  
Wei He ◽  
Liangpei Zhang ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan
Keyword(s):  
Image Restoration ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Matrix Recovery ◽  
Rank Matrix ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

scholarly journals Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation

2021 ◽  
Vol 13 (4) ◽  
pp. 827
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Hyperspectral Image ◽  
Noise Removal ◽  
Low Rank ◽  
Matrix Recovery ◽  
Tv Regularization ◽  
Rank Matrix ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.

scholarly journals Classification with incomplete training information using a cluster ensemble and low-rank matrix decomposition

2020 ◽  
Vol 4 (78) ◽  
pp. 54-62
Author(s):  
B. A. TULEGENOVA ◽  
◽  
E. N. AMIRGALIYEV ◽  
L. SH. CHERIKBAYEVA ◽  
V. B. BERIKOV ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Cluster Ensemble ◽  
Pattern Recognition Problem ◽  
Rank Matrix ◽  
Rank Decomposition ◽  
Low Rank Matrix ◽  
Laplacian Regularization ◽  
Association Matrix

The paper is devoted to solve the pattern recognition problem with incomplete learning data. The solution method, which combines similarity graph with Laplacian Regularization and collective clustering is proposed. The low-rank decomposition of co-association matrix for cluster ensemble is used, which allows to speed up the computations and keep memory. Experimental results on test tasks and on real hyperspectral image demonstrate the effectiveness of proposed method, including with noisy data.

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