Bayesian compressive sensing using reweighted laplace priors

Jiang, Tao; Zhang, Xiaowei; Li, Yingsong

doi:10.1016/j.aeue.2018.10.005

Cited by 11 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, the sensed signals are measured by Gaussian random matrices Φ j ∈ R M x ×400 in each time instant τ = 31 s, and the sink received the measured signals. The simulation results are reported from the obtained mean results of 100 frames with different x j s. The performance of the proposed reconstruction algorithm is compared with gradient-CS [11], SFAR-2D [12], reweightedlaplace [13], sequential-CS [20], modified-CS [29] and regularized modified-BPDN [30]. During the simulation, several parameters of the algorithms have been carefully tuned to perform an impartial comparison between the algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…And as the number of measurements increases, the performance of the methods gradually approaches that of the proposed algorithm. The reconstruction times versus different measurement numbers for gradient-CS [11], SFAR-2D [12], reweighted-laplace [13], sequential-CS [20], modified-CS [29], regularized modified-BPDN [30] and the proposed algorithm is depicted in Fig. 2.…”

Section: Resultsmentioning

confidence: 99%

“…Due to the main challenges of the energy constraint WSNs, reducing the overall energy consumption of the sensor nodes is of great importance. In this respect, compressive sensing (CS) has drawn much interest, simultaneously providing a sensing and compression framework [1][2][3][4][5][6][7][8][9], enabling a potentially significant reduction in the sampling and computational costs of a sensor with limited capabilities; while still guaranteeing perfect recovery [10][11][12][13]. Although CS has established a promising performance in exploiting the spatial or temporal correlation, the necessary data transmission can be decreased more.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Compressed Sensing-Based Recovery Algorithm for Wireless Sensor Networks

Jahanshahi¹,

Danyali²,

Helfroush³

2019

RADIOENGINEERING

View full text Add to dashboard Cite

In this paper, a novel compressed sensing (CS) acquisition and joint recovery of spatiotemporal correlated signals algorithm is presented for effective data collection and precise sensors data streams reconstruction in wireless sensor networks. The CS-based proposed method utilizes an iterative re-weighted 1-minimization and a 2 regularization to increase the reconstruction accuracy with a small number of required data transmission. Moreover, we develop an alternating direction method of multipliers based algorithm to efficiently solve the resulting optimization problem. Numerical experiments are conducted on several test signals with a variety of sampling ratios. The experimental results verify the effectiveness of the proposed scheme in terms of reconstruction accuracy and consumption time compared with the state of the art algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Compressed Sensing-Based Recovery Algorithm for Wireless Sensor Networks

Jahanshahi¹,

Danyali²,

Helfroush³

2019

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…In Ref. [26], the reweighted Laplace priors are developed to improve the sparsity representation. However, in this work, the calculation of matrix inversion will cause significant time consuming.…”

Section: Introductionmentioning

confidence: 99%

Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior

Zhang

et al. 2022

IET Signal Processing

View full text Add to dashboard Cite

Bayesian compressive sensing (BCS) is an important sub-class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub-optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex-valued signal processing field.

show abstract

“…Research on high performance CS image reconstruction methods is a hot topic in CS field. Traditional methods [8][9][10][11][12][13] to solve CS reconstruction problem are mostly based on physical-driven approaches, such as convex optimization [8,11], greedy algorithm [12,13], and non-convex algorithm [9,10]. However, these methods commonly adopt iterative optimization strategies to solve the problem of image signal reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

2021

View full text Add to dashboard Cite

Compressive sensing (CS) technology is introduced into space optical remote sensing image acquisition stage, which could make wireless image sensor network node quickly and accurately obtain images in the case of two constraints of limited battery power and expensive sensor costs. On this basis, in order to further improve the quality of CS image reconstruction, we propose fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet) for image reconstruction. FFPL-EDRNet consists of a convolution layer and a reconstruction network. We train FFPL-EDRNet end-to-end, thus greatly simplifying the pre-processing and post-processing process and eliminating the block effect of reconstructed images. The reconstruction network is based on residual network, which introduces multiscale feature extraction, multi-scale feature combination and multi-level feature combination. Feature fusion integrates low-level information with high-level information to reduce reconstruction error. The perceptual loss function based on pretrained InceptionV3 uses the weighted mean square error to define the loss value between the reconstructed image feature and the label image feature, which makes the reconstructed image more semantically similar to label image. In the measurement procedure, we use convolution to achieve block compression measurement, so as to obtain full image measurements. For image reconstruction, we firstly use a deconvolution layer to initially reconstruct the image and then use the residual network to refine the initial reconstructed image. The experimental results show that: in the case of measurement rates (MRs) of 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio (PSNR) = 27.502, 26.804, 24.593, 21.359 and structural similarity (SSIM) = 0.842, 0.816, 0.720, 0.568 of the reconstructed images obtained by FFPL-EDRNet. Therefore, Our FFPL-EDRNet could enhance the quality of image reconstruction.

show abstract

Bayesian compressive sensing using reweighted laplace priors

Cited by 11 publications

References 26 publications

A Modified Compressed Sensing-Based Recovery Algorithm for Wireless Sensor Networks

A Modified Compressed Sensing-Based Recovery Algorithm for Wireless Sensor Networks

Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior

Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

Contact Info

Product

Resources

About