On some common compressive sensing recovery algorithms and applications

Draganic, Andjela; Orović, Irena; Stanković, Srdjan

doi:10.2298/fuee1704477d

Cited by 23 publications

(22 citation statements)

References 56 publications

(102 reference statements)

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“…This can be seen as the constraint l1 norm minimization problem. It is solvable by many known algorithms used in compressive sensing 31 .…”

Section: Resultsmentioning

confidence: 99%

“…This can be seen as the constraint l 1 norm minimization problem. It is solvable by many known algorithms used in compressive sensing 31 . In the next part we analyze the complexity of particular algorithms adapted to minimize the computational costs and memory requirements.…”

Section: Resultsmentioning

confidence: 99%

“…The counterpart of this theorem for Gaussian boson sampling is discussed in the Discussion and implemented in 22 . In our approach we use the compressive sensing methods [26][27][28][29][30][31][32] to recover the joint sparse distribution from marginal ones. We show how to do that efficiently.…”

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confidence: 99%

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Classical simulation of boson sampling with sparse output

Roga

Takeoka

2020

Sci Rep

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Boson sampling can simulate physical problems for which classical simulations are inefficient. However, not all problems simulated by boson sampling are classically intractable. We show explicit classical methods of finding boson sampling distributions when they are known to be highly sparse. In the methods, we first determine a few distributions from restricted number of detectors and then recover the full one using compressive sensing techniques. In general, the latter step could be of high complexity. However, we show that this problem can be reduced to solving an Ising model which under certain conditions can be done in polynomial time. Various extensions are discussed including a version involving quantum annealing. Hence, our results impact the understanding of the class of classically calculable problems. We indicate that boson samplers may be advantageous in dealing with problems which are not highly sparse. Finally, we suggest a hybrid method for problems of intermediate sparsity.

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“…This can be seen as the constraint l1 norm minimization problem. It is solvable by many known algorithms used in compressive sensing 31 .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Classical simulation of boson sampling with sparse output

Roga

Takeoka

2020

Sci Rep

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“…Orthogonal Matching Pursuit [3], [4], [23] evolved from Matching Pursuit as an improvement. Hence, they share many of the properties.…”

Section: A (Nearly) Orthogonal Matching Pursuitmentioning

confidence: 99%

Comparison of some Commonly used Algorithms for Sparse Signal Reconstruction

Ratkovic

Resetar

Zecevic

2019

2019 8th Mediterranean Conference on Embedded Computing (MECO)

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Due to excessive need for faster propagations of signals and necessity to reduce number of measurements and rapidly increase efficiency, new sensing theories have been proposed. Conventional sampling approaches that follow Shannon-Nyquist theorem require the sampling rate to be at least twice the maximum frequency of the signal. This has triggered scientists to examine the possibilities of creating a new path for recovering signals using much less samples and therefore speeding up the process and satisfying the need for faster realization. As a result the compressive sensing approach has emerged. This breakthrough makes signal processing and reconstruction much easier, not to mention that is has a vast variety of applications. In this paper some of the commonly used algorithms for sparse signal recovery are compared. The reconstruction accuracy, mean squared error and the execution time are compared.

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“…In this domain, there is just a small number of coefficients with large values, while the rest are zero or close to zero. The incoherence can be accomplished by the random acquiring the signal samples form the dense signal domain [8], [10], [12], [16].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Recognition under Missing Image Pixels Scenario

Brajovic

Tomovic

Radonjic

2019

2019 8th Mediterranean Conference on Embedded Computing (MECO)

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This work observed the problem of fingerprint image recognition in the case of missing pixels from the original image. The possibility of missing pixels recovery is tested by applying the Compressive Sensing approach. Namely, different percentage of missing pixels is observed and the image reconstruction is done by applying commonly used approach for sparse image reconstruction. The theory is verified by experiments, showing successful image reconstruction and later person identification even if less then 90% of the image pixels is missing.

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On some common compressive sensing recovery algorithms and applications

Cited by 23 publications

References 56 publications

Classical simulation of boson sampling with sparse output

Classical simulation of boson sampling with sparse output

Comparison of some Commonly used Algorithms for Sparse Signal Reconstruction

Fingerprint Recognition under Missing Image Pixels Scenario

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