Joint Mixed-Noise Removal and Compressed Sensing Reconstruction of Hyperspectral Images via Convex Optimization

Takeyama, Saori; Ona, Shunsuke

doi:10.1109/igarss39084.2020.9323489

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…However, in practical problems, noise is unavoidable. Takeyama et al 15 proposed a CS reconstruction model that can deal with mixed Gaussian noise. The method describes the reconstructed model as a convex optimization model, and HSSTV is used as the regularization term.…”

Section: Hyperspectral Image Reconstruction Using Improved Optimizati...mentioning

confidence: 99%

Survey on compressed sensing reconstruction method for3Ddata

Zhang

Xie

2022

Concurrency and Computation

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The information society has higher and higher requirements for the collection, transmission and storage of digital signals, and signal utilization efficiency has become an increasingly important part of the digital signal processing process. The traditional digital signal processing needs to satisfy the Nyquist sampling theorem to ensure the restoration of the signal, while the digital signal processing method based on compressed sensing can sample and reconstruct the signal under the conditions that much lower than the Nyquist sampling theorem. Undoubtedly, the utilization efficiency of digital signals has been greatly accelerated. In this article, taking hyperspectral images and videos as examples, we review the basic theory, reconstruction model, and reconstruction algorithm of 3D data compressed sensing. We analyze, summarize, and discuss the existing literature. Finally, the research status of compressive sensing reconstruction methods for hyperspectral images and videos is compared, and several important research directions for compressive sensing reconstruction of 3D data in the future are proposed.

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Section: Hyperspectral Image Reconstruction Using Improved Optimizati...mentioning

confidence: 99%

Survey on compressed sensing reconstruction method for3Ddata

Zhang

Xie

2022

Concurrency and Computation

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“…However, it becomes possible in the CS theory for the signal to be recovered with fewer samples under the circumstance of known sparsity. The CS theory is widely utilized in many applications, such as signal processing [1][2][3][4], image processing [5][6][7][8][9], magnetic resonance imaging (MRI) [10][11][12][13][14], and target imaging [15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Imaging of Passive Intermodulation Sources Based on Virtual Array Expansion Synchronous Imaging Compressed Sensing

et al. 2023

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Interference caused by passive intermodulation (PIM) has seriously affected system performance. Compressed sensing (CS) can be used to locate radial PIMs. In CS-based electromagnetic imaging methods, the ability to locate the number of targets is usually limited by the number of receiving antennas, and there are flooding problems in the imaging results. To solve these problems, traditional methods are used to increase the number of receiving antennas. To reduce the number of receiving antennas and improve the imaging effect, this paper proposes a virtual array expansion synchronous imaging compressed sensing (SI-CS) method. With the full-wave electromagnetic simulation demonstrations, the proposed method exhibits significant improvement in imaging performance compared to the conventional methods. In addition, the proposed method can discriminate between targets in the presence of the flooding problem in which the paraflap is too large and the location of the PIM source cannot be determined by the amplitude value.

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“…By dividing HSI into many blocks, Zhang et al [ 5 ] introduced an HSI data reconstruction method based on low-rank matrix recovery (LRMR). In particular, for this kind of signal, many new compressive sensing (CS)-based methods [ 9 , 10 , 11 ] have been proposed [ 6 , 8 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Golbabaee et al [ 6 ] simultaneously reconstructed HSI data with a low-rank and joint-sparse (L&S) structure by assuming that HSI data are low-rank and using a spatially joint-sparse wavelet representation.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Huang

et al. 2022

Sensors

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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.

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Joint Mixed-Noise Removal and Compressed Sensing Reconstruction of Hyperspectral Images via Convex Optimization

Cited by 7 publications

References 23 publications

Survey on compressed sensing reconstruction method for3Ddata

Survey on compressed sensing reconstruction method for3Ddata

Electromagnetic Imaging of Passive Intermodulation Sources Based on Virtual Array Expansion Synchronous Imaging Compressed Sensing

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

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