Object Specific Image Reconstruction using a Compressive Sensing Architecture for Application in Surveillance Systems

Mahalanobis, Abhijit; Muise, Robert

doi:10.1109/taes.2009.5259191

Cited by 55 publications

(48 citation statements)

References 14 publications

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“…However, all of these iterative or recursive algorithms require hardware implementations of high complexity and thus the gain by the use of a lower cost/speed/power Analog Digital Converters (ADC) is often cancelled. Surveillance applications can take advantage of CS techniques as described in [10]. Sensor networks can also benefit from CS algorithms [11].…”

Section: Open Accessmentioning

confidence: 99%

Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Petrellis¹

2014

Applied Sciences

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Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem. In this paper, an undersampling technique is presented that allows the reconstruction of the sparse information that is transmitted through Orthogonal Frequency Division Multiplexing (OFDM) modulation. The properties of the Discrete Fourier Transform (DFT) that is employed by the OFDM modulation, allow the estimation of several samples from others that have already been obtained on the side of the receiver, provided that special relations are valid between the original data values. The inherent sparseness of the original data, as well as the Forward Error Correction (FEC) techniques employed, can assist the information recovery from fewer samples. It will be shown that up to 1/4 of the samples can be omitted from the sampling process and substituted by others on the side of the receiver for the successful reconstruction of the original data. In this way, the size of the buffer memory used for sample storage, as well as the storage requirements of the Fast Fourier Transform (FFT) implementation at the receiver, may be reduced by up to 25%. The power consumption of the Analog Digital Converter on the side of the receiver is also reduced when a lower sampling rate is used.

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Section: Open Accessmentioning

confidence: 99%

Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Petrellis¹

2014

Applied Sciences

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“…A large number of resources are required for the hardware implementation of a CS approach, as described for example, in [2,3]. The CS techniques have been extensively used in the application domain of image processing [4,5]. It is assumed that the information is concentrated at specific parts of the sparse images like radar spots in [4], or smaller areas in Correspondence: npetrellis@teilar.gr Department of Computer Science and Engineering, TEI of Thessaly, Larissa, Greece the center of the landscape in [5].…”

Section: Introductionmentioning

confidence: 99%

“…The CS techniques have been extensively used in the application domain of image processing [4,5]. It is assumed that the information is concentrated at specific parts of the sparse images like radar spots in [4], or smaller areas in Correspondence: npetrellis@teilar.gr Department of Computer Science and Engineering, TEI of Thessaly, Larissa, Greece the center of the landscape in [5]. The efficiency of the CS approach used in [4] or [5] depends on these image features.…”

Section: Introductionmentioning

confidence: 99%

Optimal reception of sub-sampled time-domain sparse signals in wired/wireless OFDM transceivers

Petrellis

2016

J Wireless Com Network

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A time domain sub-sampling technique that can be applied to wired or wireless orthogonal frequency division multiplexing systems is described in this paper. Focus is given on the specific conditions that allow optimal sparse information recovery. The advantage of the proposed method is that it can be implemented with very low complexity hardware, since it is based on deterministic non-iterative operations. A sub-sampling operating mode is used when sparse information is detected by the receiver and can be applied up to 75 % of the time. The power consumed by the various modules can be significantly reduced during the sub-sampling mode. These modules include the analogue/digital converter and the (inverse) Fast Fourier Transform, as well as the buffering memory used by these modules. The signal to noise ratio analysis shows that an optimal signal reception can be achieved if low order quadrature amplitude modulation and Fourier Transform size are used. Full information recovery can be achieved in some cases of wired communication. A bit error rate lower than 10 −3 is measured, if fewer than 1/16 of the Fourier Transform input symbols are omitted at the receiver. A space-time block code system is also modelled, to test the proposed sub-sampling method in a wireless environment.

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“…Object specific reconstruction based on the reconstructed algorithm for surveillance was proposed in [5]. The different sensing matrices implemented in [3] are based on [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Block-Based Compressive Sensed Thermal Image Reconstruction using Greedy Algorithms

Dias¹,

Rane²

2014

IJIGSP

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Abstract-This paper implements a block based compressive sensing technique for thermal image reconstruction using greedy algorithms. A total of fourteen different sensing patterns were tested for data acquisition. Orthogonal Matching Pursuit (OMP) and Regularized Orthogonal Matching Pursuit (ROMP) with two different thresholds were implemented for image reconstruction with OMP having an edge over ROMP in terms of error and PSNR. ROMP was faster in terms of iterations needed for reconstruction. As the threshold for ROMP was increased the number of iterations needed decreased. Gaussian, Bernoulli and Hadamard patterns were the best for reconstruction. Hadamard matrix, Bernoulli matrix with +/-1 entries and Bernoulli matrix with 0/1 entries have the added advantage of being more conducive for hardware implementation. This paper used Discrete Cosine Transform as the sparsifying basis for reconstruction.

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Object Specific Image Reconstruction using a Compressive Sensing Architecture for Application in Surveillance Systems

Cited by 55 publications

References 14 publications

Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Optimal reception of sub-sampled time-domain sparse signals in wired/wireless OFDM transceivers

Block-Based Compressive Sensed Thermal Image Reconstruction using Greedy Algorithms

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