Compressed Sensing Reconstruction Algorithm Research Articles

Computer simulation and modeling of glow discharge optical emission coded aperture elemental mapping

BackgroundElemental mapping (EM) yields necessary insights into mechanisms of interest in solid samples across multiple disciplines. There are several EM techniques available but long acquisition time is a common limitation. Glow discharge optical emission spectroscopy (GDOES) allows direct quantitative multi-EM at very high throughput (∼10 s s) when coupled to traditional hyperspectral imaging (HSI) techniques. However, GDOES consumes the sample via sputtering, such that traditional HSI sequential scanning requirements lead to loss of information/resolution, which is compounded for multi-EM and limits nanomaterials analysis. Thus, there is a need for faster HSI to enable GDOES multi-EM of nanoscale materials. ResultsHere, a new technique is described, Glow discharge Optical emission Coded Aperture elemental Mapping (GOCAM), that takes advantage of compressive coded aperture spectral imaging to enable multi-EM in a single camera exposure. In this first phase of development, computer model simulations were implemented to study the effects of coded aperture parameters on data fidelity, which showed the best fidelity is achieved at smaller mask element sizes and transmittance of 60 %. In addition, SeSCIGPU demonstrated the best fidelity performance compared to several compressed sensing reconstruction algorithms, including TwIST, GAP-TV, SeSCICPU, and ADMM-TV, as evaluated by studying the effects of varying the corresponding hyperparameters. SignificanceThis study shows GOCAM's feasibility and provides a starting point for the second phase hardware development currently underway. GOCAM's potential to allow multi-EM from solid surfaces in a fraction of a second will be particularly enabling for nanostructured materials characterization.

A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection.

Fast reconstruction of EEG signal compression sensing based on deep learning

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals.

A temporal Convolutional Network for EMG compressed sensing reconstruction

Electromyography (EMG) plays a vital role in detecting medical abnormalities and analyzing the biomechanics of human or animal movements. However, long-term EMG signal monitoring will increase the bandwidth requirements and transmission system burden. Compressed sensing (CS) is attractive for resource-limited EMG signal monitoring. However, traditional CS reconstruction algorithms require prior knowledge of the signal, and the reconstruction process is inefficient. To solve this problem, this paper proposed a reconstruction algorithm based on deep learning, which combines the Temporal Convolutional Network (TCN) and the fully connected layer to learn the mapping relationship between the compressed measurement value and the original signal, and it has been verified in the Ninapro database. The results show that, for the same subject, compared with the traditional reconstruction algorithms orthogonal matching pursuit (OMP), basis pursuit (BP), and Modified Compressive Sampling Matching Pursuit (MCo), the reconstruction quality and efficiency of the proposed method is significantly improved under various compression ratios (CR).

Ghost imaging based on Fermat spiral laser array designed for remote sensing.

We propose a Fermat spiral laser array as illumination source in ghost imaging. Due to the aperiodic structure, the Fermat spiral laser array generates illuminating light field without spatial periodicity on the normalized second-order intensity correlation function. A single-pixel detector is used to receive the signal light from object for image reconstruction. The effects of laser array parameters on the quality of ghost imaging are analyzed comprehensively. Through experimental demonstration, the Fermat spiral laser array successfully achieves ghost imaging with high quality by combining with the compressive sensing reconstruction algorithm. This method is expected to be applied in remote sensing by combining with phased and collimated fiber laser array equipped with the high emitting power and high-speed modulation frequency.

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

Orthogonal matching pursuit algorithm based on weighted cosine similarity for sound source localization

Abstract Compressive sensing overcomes the limitations of the Nyquist criteria and is one of the most widely used compressive sensing reconstruction algorithms. Orthogonal matching pursuit (OMP) algorithm is simple, in terms of hardware implementation, and has high computational efficiency. However, the OMP algorithm exhibits poor identification performance for low-frequency sound sources and results in large localization deviations when the mesh spacing of the focus plane is small. In this study, a novel atom selection criterion based on weighted cosine similarity was proposed to improve the OMP algorithm for sound source localization and characterization. This method replaces the original inner product criterion to measure the correlation between the column vectors of the sensing matrix and the residuals, which addresses the atom selection error caused by the high correlation between atoms. Numerical simulations and experimental results show that the proposed method has a stronger anti-noise interference capability and higher accuracy for sound source identification with fewer sampling points, particularly in low-frequency and low signal-to-noise ratio environments. Compared to other OMP algorithms, the proposed method improves the performance of the OMP algorithm in sound source localization and widens the sound frequency range. This study is valuable for achieving highly accurate sound source localization and reducing measurement costs in practical applications.

Compressed sensing framework for BCG signals based on the optical fiber sensor.

A compressed sensing (CS) framework is built for ballistocardiography (BCG) signals, which contains two parts of an optical fiber sensor-based heart monitoring system with a CS module and an end-to-end deep learning-based reconstruction algorithm. The heart monitoring system collects BCG data, and then compresses and transmits the data through the CS module at the sensing end. The deep learning-based algorithm reconstructs compressed data at the received end. To evaluate results, three traditional CS reconstruction algorithms and a deep learning method are adopted as references to reconstruct the compressed BCG data with different compression ratios (CRs). Results show that our framework can reconstruct signals successfully when the CR grows from 50% to 95% and outperforms other methods at high CRs. The mean absolute error (MAE) of the estimated heartbeat rate (HR) is lower than 1 bpm when the CR is below 95%. The proposed CS framework for BCG signals can be integrated into the IoMT system, which has great potential in health care for both medical and home use.

Efficient Compressed Sensing Reconstruction Algorithm for Nonnegative Vectors in Wireless Data Transmission

With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm.

Retracted: The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images

Retracted: The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images

Preface

The 2nd International Conference on Electronics and Integrated Circuit Technology (EICT 2023) was held in Chengdu, China from February 24th to 26th, 2023 (virtual conference). The first workshop of the series was held in Shenzhen in virtual form last year. The purpose of EICT 2023 was to discuss the present status and future perspectives of the electronics and integrated circuit technology. The following five topics were selected in order to cover most of the scientific program and highlight an area where new ideas have emerged over recent years:(1) Electronic Science and Technology;(2) Integrated Circuit Design and Integrated System;(3) Electronic Materials and Devices;(4) Digital Information Processing;(5) Magneto-electronic Materials and Applications, etc.There were about 60 participants. In addition to the keynote speeches, we had many talks selected from contributed papers. There were invited, oral, and poster sessions. This proceedings contains the papers presented in invited and selected talks together with those presented in poster sessions.At the Conference, the participants discussed a wide range of issues affecting the theoretical and computational aspects of research problems in the electronics and integrated circuit technology. Besides, we had a comprehensive overview of this fascinating field and of future scenarios thanks to the participation of leaders of the most important projects. Associate Professor Cheng Zhang from Anhui University, China performed a keynote speech and shared with us his own research experience and professional view with the report Compressed Sensing and Deep Learning for Optical Imaging. Compressive Sensing (CS) provides an innovative sampling framework based on the rate of information of a signal rather than the bandwidth, which enables efficient data sampling at a rate much below the Shannon Nyquist rate, thereby minimizing redundancy during sampling. The emerging of Deep Learning (DL) naturally complements the practical limitations with the CS sampling and reconstruction algorithms and provides a powerful framework to address the most practical limitations to achieve higher-accuracy reconstructions from extremely fewer measurements by adapting the sampling matrix, reconstructing the signal, learning from the compressed samples, etc.We wish to acknowledge our host institutions, sponsors, and the many people whose work made this Conference possible. We would like to express our gratitude to the members of the Technical Program Committee and those of the Organizing Committee for their efforts which made this Workshop successful. In particular we would like to present our great thanks to the editors and other colleagues of Journal of Physics: Conference Series for their patience and kind assistance in publishing this paper volume.The Committee of EICT 2023.List of Committee Member is available in the pdf.

Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function

Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8-9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8-9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8-9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8-9 RSIs and the boundaries of the transition areas.

Compressed sensing approach for CMUT sparse array in multi-element synthetic transmit aperture

In order to improve the imaging quality of capacitive micromachined ultrasonic transducer (CMUT) sparse array, the authors propose an imaging strategy combining multi-element synthetic transmit aperture (MSTA) imaging mode and compressed sensing (CS) framework. The imaging strategy consists of four steps: First, use the genetic algorithm (GA) to sparse the CMUT array and obtain the position of the receiving active array elements. Second, the CMUT sub-arrays are used to transmit ultrasonic signal in turn, and the sparse array obtained in the first step is used to receive ultrasonic echoes. Third, the received under-sampling channel data are used to recover the full channel data with the CS reconstruction algorithm. Finally, an MSTA image is beamformed from the recovered full channel data. This imaging strategy is called MSTA-CS mode. Due to the efficient sparse recovery capability of CS technology, the MSTA-CS imaging strategy not only reduces the number of transmitting hardware channels, but also improves the imaging clarity of CMUT sparse array. Both the Field II simulation and the breast phantom experiments demonstrate that the MSTA-CS mode achieves higher contrast and structural similarity under the same receiving sparse array conditions. • In this paper, Multi-element Synthetic Transmit Aperture (MSTA) method is used in ultrasound imaging of CMUT sparse array, this method reduces the number of transmitting hardware channels. • Under the condition of lateral under-sampling of CMUT sparse array, the closed array elements echoes data are recovered by using compressed sensing framework. • Take the full array imaging results as the standard, and verify the compressed sensing theory through breast phantom experiments.

Compressed ultrahigh-speed single-pixel imaging by swept aggregate patterns

Single-pixel imaging (SPI) has emerged as a powerful technique that uses coded wide-field illumination with sampling by a single-point detector. Most SPI systems are limited by the refresh rates of digital micromirror devices (DMDs) and time-consuming iterations in compressed-sensing (CS)-based reconstruction. Recent efforts in overcoming the speed limit in SPI, such as the use of fast-moving mechanical masks, suffer from low reconfigurability and/or reduced accuracy. To address these challenges, we develop SPI accelerated via swept aggregate patterns (SPI-ASAP) that combines a DMD with laser scanning hardware to achieve pattern projection rates of up to 14.1 MHz and tunable frame sizes of up to 101×103 pixels. Meanwhile, leveraging the structural properties of S-cyclic matrices, a lightweight CS reconstruction algorithm, fully compatible with parallel computing, is developed for real-time video streaming at 100 frames per second (fps). SPI-ASAP allows reconfigurable imaging in both transmission and reflection modes, dynamic imaging under strong ambient light, and offline ultrahigh-speed imaging at speeds of up to 12,000 fps.

A High-Resolution and Low-Cost Entangled Photon Quantum Imaging Framework for Marine Turbulence Environment

Entangled photon quantum imaging technology has become very promising for marine imaging due to its high resolution, anti-turbulence, and anti-interference properties. The turbulence and optical attenuation caused by oceanic water would impose severe limitations on conventional imaging equipment. Quantum imaging based on entangled light sources could make it possible to overcome the constraints of low imaging resolution, large maritime environmental impact and low confidentiality in traditional underwater target imaging. Hence, the recent technological developments in terms of quantum imaging in free space motivate our investigation into high-resolution quantum imaging based on spatially correlated properties of entangled photon pairs in a marine environment. Moreover we propose and design an innovative underwater quantum imaging optical path for experimental verification. We conduct extensive experiments to assess the effectiveness of our proposed quantum imaging approach in marine environments. In order to solve the problem of the slow quantum imaging process, we propose an innovative compressive sensing reconstruction algorithm. Experiments show that the compressive sensing method could be utilized to reduce the imaging time. Finally, we propose an underwater quantum target identification method based on deep learning, further providing the possibility for underwater quantum target identification of entangled light sources.

Super-resolution fluorescence blinking imaging using compressive sensing

Super-resolution optical fluctuation imaging (SOFI) is an extensively used super-resolution (SR) imaging technique. The sample condition and imaging system are simpler than most SR systems, and the cusp-artifacts limit the resolution of high-order SOFI. To improve the resolution of SOFI, an alternative method is to improve the reconstruction algorithm. Here, compressive sensing (CS) is used in SOFI to improve its resolution. The detailed CS reconstruction algorithm is chosen as multiple measurement vector model sparse Bayesian learning (MSBL). We demonstrate that MSBL can achieve higher than threefold resolution improvement in simulation under certain conditions. The SOFI experiment analysis demonstrates that MSBL can improve the resolution about 2.5-fold compared with the diffraction limit. All results prove that MSBL has a higher resolving ability compared with other algorithms. Our results prove that CS is a broad applicability tool for most of the existing SR microscopy techniques and can be applied in 3D and live-cell SR fluorescence microscopy imaging in the future.

Adaptive compressed sensing algorithm for terahertz spectral image reconstruction based on residual learning

Terahertz time-domain spectroscopy (THz-TDS) is widely applied in the field of rapid nondestructive detection of grain owing to its low photon energy and high penetrating power. Nevertheless, terahertz imaging systems suffer from the problems of long image acquisition time and massive data processing. To mitigate these issues, this work presents an adaptive compressed sensing reconstruction algorithm for terahertz spectral images based on residual learning (ATResCS). The algorithm compresses the number of data samples, reducing the amount of data required for imaging and improving the imaging speed. Further, ATResCS reduces the time complexity by employing a convolutional neural network. The algorithm is validated by acquiring terahertz spectral image data via a THz-TDS system. ATResCS outperforms conventional algorithms regarding peak signal-to-noise ratio (PSNR) and structural similarity, significantly reducing the reconstruction time and, thus, enabling real-time reconstruction. Specifically, at low sampling rates (0.1), ATResCS retains key spectral image information. The average PSNR is 0.96 – 1.015 dB higher than that of DR2-Net, reducing the average reconstruction time by 0.1 – 0.2 s. Experiments demonstrate that ATResCS has better reconfiguration capability and lower algorithm complexity, enabling high-quality and fast reconstruction of terahertz spectral images.

A Compressed Sensing Reconstruction Algorithm Based on Hyperbolic Function

The accuracy of the compressed sensing theory reconstruction algorithm is important for signal recovery. Sparsity Adaptive Matching Pursuit (SAMP) has a fixed step size in each iterative process of reconstruction, which has a significant impact on accuracy in actual use, often leading to overestimation and underestimation. In order to solve this problem, combined with the advantages of regular backtracking and variable step size, the article introduces the idea of retrospective in the atom selection stage. Then, the atoms are inspected using backtracking and re-screened. The algorithm uses regularization at the beginning to select atoms with higher energy and reduce the number of atoms in the candidate set at the threshold stage. During reconstruction, because the energy difference of the reconstructed signal decreases rapidly at the initial stage, and then the energy difference decreases slowly. This algorithm takes advantage of the large change rate of the hyperbolic function at the beginning and the slower change rate at the later stage. At first, a large step size is used. When the energy difference reaches a certain threshold, a small step size is used instead. After the actual simulation, the improved algorithm improves the reconstruction accuracy and the effect is better.

An adaptive beamforming algorithm for sound source localisation via hybrid compressive sensing reconstruction

In this study, a hybrid compressive sensing reconstruction algorithm called SAMP-CoSaMP is proposed. The unique combination maintains the speed advantage of CoSaMP and the adaptive sparsity searching ability from the SAMP. Afterwards, an improved beamforming algorithm named SC-DAMAS for sound source localisation is created by integrating our hybrid algorithm with the classic DAMAS. Lastly, the reconstruction accuracy is compared between the SAMP-CoSaMP, SAMP, and CoSaMP algorithms in different signal-to-noise ratio scenarios. The results show that the SAMP-CoSaMP is balanced between running efficiency and reconstruction error. In addition, we perform comparative sound source localisation simulations and experiments by our SC-DAMAS with those of the conventional beamforming method and orthogonal matching pursuit algorithm-based deconvolution approach. SC-DAMAS is superior to the aforementioned counterparts in localisation performance without the need to predetermine the sparsity value.

CSIE-M: Compressive Sensing Image Enhancement Using Multiple Reconstructed Signals for Internet of Things Surveillance Systems

Artificial intelligence of things has brought artificial intelligence to the cutting-edge Internet of Things. In recent years, compressive sensing (CS), which relies on sparsity, is widely embedded and expected to bring more energy efficiency and a longer battery lifetime to IoT devices. Different from the other image compression standards, CS can get various reconstructed images by applying different reconstruction algorithms on coded data. Using this property, it is the first time to propose a deep learning based compressive sensing image enhancement framework using multiple reconstructed signals (CSIE-M). In this article, first, images are reconstructed by different CS reconstruction algorithms. Second, reconstructed images are assessed and sorted by a no-reference quality assessment module before being input to the quality enhancement module by order of quality scores. Finally, a multiple-input recurrent dense residual network is designed for exploiting and enriching the useful information from the reconstructed images. Experimental results show that CSIE-M obtains 1.88–8.07 dB peek-signal-to-noise (PSNR) improvement while the state-of-the-art works achieve a 1.69–6.69 dB PSNR improvement under sampling rates from 0.125 to 0.75. On the other hand, using multiple reconstructed versions of the signal can improve 0.19–0.23 dB PSNR, and only 4% reconstructing time is increasing compared to using a reconstructed signal.