Compressive sensing for images using a variant of Toeplitz matrix for wireless sensor networks

Nandhini, S. Aasha; Radha, S.; Nirmala, P.; Kishore, Rajalekshmi

doi:10.1007/s11554-016-0658-z

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…A combination of Toeplitz, Hankel, and circulant matrices was proposed in [ 26 ]. First, the Toeplitz matrix is constructed from Gaussian random entries.…”

Section: Related Workmentioning

confidence: 99%

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An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

Skosana

Abu‐Mahfouz

2023

Sensors

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A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications.

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“…A combination of Toeplitz, Hankel, and circulant matrices was proposed in [ 26 ]. First, the Toeplitz matrix is constructed from Gaussian random entries.…”

Section: Related Workmentioning

confidence: 99%

“…Determinsistic sensing matrices have outperformed dense ones in many studies [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Semi-deterministic matrices are being displaced in popularity by fully deterministic ones, particularly matrices based on chaotic sequences [ 24 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

Skosana

Abu‐Mahfouz

2023

Sensors

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“…The CS technique has been widely applied in image processing, wireless sensor network, wireless communications, etc. [1][2][3]. The application of CS can reduce the transmitted image data volume and power consumption of electronic devices.…”

Section: Introductionmentioning

confidence: 99%

Compressed image transmission in precoded OFDM VLC systems

Wang

Hou

Wang

2022

Preprint

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In this paper, a compressed image transmission scheme is proposed in a precoded orthogonal frequency division multiplexing (OFDM)-based visible light communication (VLC) system. First, an image is divided into sub-block images; then, each block image is sampled by block-based compressive sensing (BCS) based on the Hadamard sparse basis matrix with a partial DCT measurement matrix. The resulting data are transformed into bit stream, which is transmitted in a DFT precoded OFDM-based VLC. We propose an integrated end-to-end OFDM-based VLC system platform, including BCS, quantization, VLC link, and OFDM transmission. The simulation results prove the effectiveness of the proposed simulation platform. The bit error rate (BER) and peak signal-to-noise ratio (PSNR) performances of the simulation platform are evaluated as well. The simulation results show that different images result in different BER and PSNR performances in the proposed precoded OFDM-VLC system. The larger the information entropy of images, the better the PSNR and BER performances. In addition, the PAPR of the DFT precoded OFDM can be significantly reduced compared to that of the conventional OFDM for compressed image data.

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“…The sparsity of the signal in its transformed versions are two types. One of them is, over some dictionary formed analytically by DCT and wavelet transformation of itself [9][10] [11] and the other is over some learned dictionary designed by prior knowledge of several correlated signals [12][13] [14][15] [16]. Compressed sensing based on the latter design approach is less loss and we found it suitable for fingerprint image sampling and compression.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing System for a Fingerprint Image Recognition Using Sensing Matrix with Chaotic Model-Based Deterministic Row Indexes

Hailemariam

Gupta

2021

Preprint

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This paper proposes a novel design approach for a secured compressed sensing system for fingerprint imaging and its transmission. In the proposed design, the first stage is acquiring the signal followed by sparsely modeling it using Orthogonal Matching Pursuit (OMP) algorithm. In addition to compressing, to guaranty its security, we multiply the sparse modeled data by a novel deterministic partially orthogonal Discrete Cosine Transform (DCT) sensing matrix. Furthermore, the construction of the sensing matrix uses a modified Multiplicative Linear Congruential Generator (MLCG) to select the row index appropriately from chaotically re-arranged rows of DCT pseudo-randomly. On the other hand, the simultaneous recovering and decryption of the compressed image accomplished with the help of a convex optimization method. The proposed system tested by employing different image and security assessment techniques. The results show that we have archived better Peak Signal to Noise Ratio (PSNR) than the recommended value for wireless transmission using samples below 25%.

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Compressive sensing for images using a variant of Toeplitz matrix for wireless sensor networks

Cited by 11 publications

References 17 publications

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

Compressed image transmission in precoded OFDM VLC systems

Compressed Sensing System for a Fingerprint Image Recognition Using Sensing Matrix with Chaotic Model-Based Deterministic Row Indexes

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