Comparison of OMP and SOMP in the reconstruction of compressively sensed hyperspectral images

Aravind, N V; Abhinandan, K. R.; Acharya, Vineeth V; David, S Sumam

doi:10.1109/iccsp.2011.5739298

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…Among thm receives significant interest nical method in his family, the d special attention due to its onstruction performance. In fact, OMP algorithm is reliable for d near-sparse signals [15]. OMP nted in this paper for the inverse P resulting images are compared ection (SBP) technique.…”

Section: µ Maxmentioning

confidence: 99%

Compressed sensing based nearfield electromagnetic imaging

Tabassum

Elshafiey

Alam

2014

2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014)

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This paper proposes a novel method of nearfield electromagnetic imaging using compressed sensing technique. Orthogonal matching pursuit (OMP) reconstruction algorithm is implemented for reconstruction of the target space. A dictionary is tested considering head imaging of single and multiple brain tumor targets. The received scattered time-domain signals are captured using spatial compressed sensing and later interpolated for full target space. These signals are also processed for temporal compressed sensing using background subtraction. Simulation of the forward problem it is conducted using CST Microwave Studio using frequency range of 300-3000 megahertz. The quality of reconstructed images reveals the potential of the proposed method.Index Terms-Nearfield imaging, electromagnetic imaging, brain tumor imaging, temporal compressed sensing, spatial compressed sensing.

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Section: µ Maxmentioning

confidence: 99%

Compressed sensing based nearfield electromagnetic imaging

Tabassum

Elshafiey

Alam

2014

2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014)

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“…In the OMP algorithm, the sparse vector is estimated individually based on a signal, while in the SOMP algorithm, it is estimated simultaneously based on several signals. Both of these algorithms try to find atoms that describe signals iteratively to satisfy the conditions mentioned in Equation (1) [42]. In these two techniques, in each iteration, an atom from the dictionary, which has the minimum spectral angle in the estimation error of a signal/signals is added as a new atom to a set of previously-selected atoms (activate atoms).…”

Section: Dictionary Learning and Joint Sparse Codingmentioning

confidence: 99%

A Sliding Window-Based Joint Sparse Representation (SWJSR) Method for Hyperspectral Anomaly Detection

2018

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In this paper, a new sliding window-based joint sparse representation (SWJSR) anomaly detector for hyperspectral data is proposed. The main contribution of this paper is to improve the judgments about the probability of anomaly presence in signals using the integration of information gathered during transition of sliding window for each pixel. In this method, each pixel experiences different spatial positions with respect to the spatial neighbors through the transition of this sliding window. In each position, an optimized local background dictionary is formed using a K-Singular Value Decomposition (K-SVD) algorithm and the recovery error of sparse estimation for each pixel is calculated using a simultaneous orthogonal matching pursuit algorithm (SOMP). Thus, the votes of each signal in terms of the anomaly presence in each spatial neighborhood are calculated and the variance of these recovery errors is considered as the detection criterion. The experimental results of the proposed SWJSR method on both synthetic and real datasets proved its higher performance compared to the Global RX (GRX), Local RX (LRX), Collaborative Representation Detector (CRD), Background Joint Sparse Representation (BJSR), Causal RX Detector (CR-RXD, CK-RXD), and Sliding Local RX(SLRX) detectors with an average efficiency improvement of about 7.5%, 14.25%,

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“…In this article, the common sparse support model is used. SOMP [31,36] is proposed as the reconstruction algorithm. SOMP is adapted from OMP.…”

Section: Dcs-sompmentioning

confidence: 99%

“…Distributed compressed sensing (DCS) [33,35,36] is developed for reconstructing the signals from two or more statistically dependent data sources. Multiple sensors measure signals which are sparse in some bases.…”

Section: Introductionmentioning

confidence: 99%

Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

Sermwuthisarn

Auethavekiat

Gansawat

et al. 2012

EURASIP J. Adv. Signal Process.

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The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.

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Comparison of OMP and SOMP in the reconstruction of compressively sensed hyperspectral images

Cited by 13 publications

References 15 publications

Compressed sensing based nearfield electromagnetic imaging

Compressed sensing based nearfield electromagnetic imaging

A Sliding Window-Based Joint Sparse Representation (SWJSR) Method for Hyperspectral Anomaly Detection

Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

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