scholarly journals Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 343
Author(s):  
Yanbin Zhang ◽  
Long-Ting Huang ◽  
Yangqing Li ◽  
Kai Zhang ◽  
Changchuan Yin
Keyword(s):  
Data Transmission ◽  
Data Model ◽  
Bayesian Learning ◽  
Hyperspectral Image ◽  
Sparse Matrix ◽  
Low Rank ◽  
Reconstruction Method ◽  
Sparse Bayesian Learning ◽  
Reconstruction Accuracy ◽  
Matrix Recovery

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.

Subspace based low rank & joint sparse matrix recovery

2014 ◽  
Author(s):  
Sampurna Biswas ◽  
Sunrita Poddar ◽  
Soura Dasgupta ◽  
Raghuraman Mudumbai ◽  
Mathews Jacob
Keyword(s):  
Sparse Matrix ◽  
Low Rank ◽  
Matrix Recovery

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.

Research on Background Modeling Based on Low-Rank Matrix Recovery

2014 ◽  
Vol 635-637 ◽  
pp. 1056-1059 ◽  
Author(s):  
Bao Yan Wang ◽  
Xin Gang Wang
Keyword(s):  
Sparse Matrix ◽  
Background Modeling ◽  
Low Rank ◽  
Complex Scene ◽  
Matrix Recovery ◽  
Rank Matrix ◽  
Training Samples ◽  
The Individual ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

Key and difficult points of background subtraction method lie in looking for an ideal background modeling under complex scene. Stacking the individual frames as columns of a big matrix, background parts can be viewed as a low-rank background matrix because of large similarity among individual frames, yet foreground parts can be viewed as a sparse matrix as foreground parts play a small role in individual frames. Thus the process of video background modeling is in fact a process of low-rank matrix recovery. Background modeling based on low-rank matrix recovery can separate foreground images from background at the same time without pre-training samples, besides, the approach is robust to illumination changes. However, there exist some shortcomings in background modeling based on low-rank matrix recovery by analyzing numerical experiments, which is developed from three aspects.

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