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

Sermwuthisarn, Parichat; Auethavekiat, Supatana; Gansawat, Duangrat; Patanavijit, Vorapoj

doi:10.1186/1687-6180-2012-34

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Sermwuthisarn et al [16] proposed a robust reconstruction method based on OMP with partially known support using subsampled compressed signal ensemble from the compressed signal with reduced Gaussian noise. The method has yielded higher peak signal-to-noise ratio at low measurement rate and better quality.…”

Section: B Orthogonal Matching Pursuit Algorithmmentioning

confidence: 99%

The implications of Compressive Sensing in signal processing

Vivek

Veenus²,

Dharun³

et al. 2015

2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT)

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Compressive Sensing is an innovative platform for signal processing, which offers more practical methods to solve the issues of voluminous useless data genera ted during the series of processes associated with conventional signal processing paradigm, which are based on the traditional sampling theorem. The Compressive Sensing theory proposes that sparse signals can be successfully reconstructed from very few sampies which are acquired at a much lower rate than the Nyquist rate. The theory is trying to combine the process of sampling, encoding and compressing into a simple and single step process. This concept has the potential to surpass the limits of Sampling Theorem and can perform better to deal with the related problems. The compressive sensing scenario is portrayed with this paper in an image processing environment. Aside from that, the paper puts an effort to analyze the processing and storage challenges associated with the conventional standards. The paper also proposes a prospective approach to resolve the issues concerned by using Compressive Sensing as an effective instrument. From this study and analysis it can be concluded that many of the challenges in the conventional methods can be defied contentedly with the help of Compressive Sensing.

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Section: B Orthogonal Matching Pursuit Algorithmmentioning

confidence: 99%

The implications of Compressive Sensing in signal processing

Vivek

Veenus²,

Dharun³

et al. 2015

2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT)

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“…That is prior support information is available from the prior knowledge, e.g. the lowest sub band wavelet coefficients are selected as nonzero components without testing them for correlation [7], [13]- [14]. Compared with the OMP, the OMP-PKS can recover � with the low measurement rate.…”

Section: Omp-pksmentioning

confidence: 99%

“…guaranties the exact recovery of x � when x � has at most K nonzero entries (such a signal is called �-sparse) [11]- [13].…”

Section: Omp Algorithmmentioning

confidence: 99%

Least Support Orthogonal Matching Pursuit Algorithm With Prior Information

Tawfic¹,

Kayhan²

2014

Journal of Applied Computer Science Methods

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This paper proposes a new fast matching pursuit technique named Partially Known Least Support Orthogonal Matching Pursuit (PKLS-OMP) which utilizes partially known support as a prior knowledge to reconstruct sparse signals from a limited number of its linear projections. The PKLS-OMP algorithm chooses optimum least part of the support at each iteration without need to test each candidate independently and incorporates prior signal information in the recovery process. We also derive sufficient condition for stable sparse signal recovery with the partially known support. Result shows that inclusion of prior information weakens the condition on the sensing matrices and needs fewer samples for successful reconstruction. Numerical experiments demonstrate that PKLS-OMP performs well compared to existing algorithms both in terms of reconstruction performance and execution time.

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“…The OMP-PKS [16] is a greedy algorithm uses for sparse image reconstruction and it is developed from the traditional OMP algorithm. It uses the sparse image that has some components more important than the others.…”

Section: B the Reconstruction Algorithmsmentioning

confidence: 99%

Watermarked Compressive Sensing Measurements Reconstructed by the Greedy Algorithms

Bash¹,

Kayhan²

2015

IJCTE

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Abstract-This paper proposes a CS based watermarking algorithm. In this algorithm, the watermarking image embeds into the compressive measurement vectors by the encoder. The measurement vectors are sparse in suitable basis. The resulting watermarked measurements recover by using both the OMP and the OMP-PKS reconstruction algorithms. Then the decoder uses to extract the watermarking image.In an experimental study, results obtained by the OMP and the OMP-PKS algorithms are compared. The results show that the OMP-PKS algorithm achieves performance superior to that of the OMP reconstruction algorithm.

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

Cited by 7 publications

References 37 publications

The implications of Compressive Sensing in signal processing

The implications of Compressive Sensing in signal processing

Least Support Orthogonal Matching Pursuit Algorithm With Prior Information

Watermarked Compressive Sensing Measurements Reconstructed by the Greedy Algorithms

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