Search

Compressed Sensing (CS) has been shown to be an effective technique for improving the resolution of inverse synthetic aperture radar (ISAR) imaging and reducing the hardware requirements of radar systems. In this paper, our focus is on the Lp (0 < p < 1) model, which is a well-known non-convex and non-Lipschitz regularization model in the field of compressed sensing. In this study, we propose a novel algorithm, namely the Accelerated Iterative Support Shrinking with Full Linearization (AISSFL) algorithm, which aims to solve the Lp regularization model for ISAR imaging. The AISSFL algorithm draws inspiration from the Majorization-Minimization (MM) iteration algorithm and integrates the principles of support shrinkage and Nestrove’s acceleration technique. The algorithm employed in this study demonstrates simplicity and efficiency. Numerical experiments demonstrate that AISSFL performs well in the field of ISAR imaging [ABSTRACT FROM AUTHOR]

Fluorine MRI is finding wider acceptance in theranostics applications where imaging of 19F hotspots of fluorinated contrast material is central. The essence of such applications is to capture ghosting‐artifact‐free images of the inherently low MR response under clinically viable conditions. To serve this purpose, this work introduces the balanced spiral spectroscopic imaging (BaSSI) sequence, which is implemented on a 3.0 T clinical scanner and is capable of generating 19F hotspot images in an efficient manner. The sequence utilizes an all‐phase‐encoded pseudo‐spiral k‐space trajectory, enabling the acquisition of broadband (80 ppm) fluorine spectra free from chemical shift ghosting. BaSSI can acquire a 64 × 64 image with 1 mm × 1 mm voxels in just 14 s, significantly outperforming typical MRSI sequences used in 1H or 31P imaging. The study employed in silico characterization to verify essential design choices such as the excitation pulse, as well as to identify the boundaries of the parameter space explored for optimization. BaSSI's performance was further benchmarked against the 3D ultrashort‐echo‐time balanced steady‐state free precession (3D UTE BSSFP) sequence, a well established method used in 19F MRI, in vitro. Both sequences underwent extensive optimization through exploration of a wide parameter space on a small phantom containing 10 μL of non‐diluted bulk perfluorooctylbromide (PFOB) prior to comparative experiments. Subsequent to optimization, BaSSI and 3D UTE BSSFP were employed to capture images of small non‐diluted bulk PFOB samples (0.10 and 0.05 μL), with variations in the number of signal averages, and thus the total scan time, in order to assess the detection sensitivities of the sequences. In these experiments, the detection sensitivity was evaluated using the Rose criterion (Rc), which provides a quantitative metric for assessing object visibility. The study further demonstrated BaSSI's utility as a (pre)clinical tool through postmortem imaging of polymer microspheres filled with PFOB in a BALB/c mouse. Anatomic localization of 19F hotspots was achieved by denoising raw data obtained with BaSSI using a filter based on the Rose criterion. These data were then successfully registered to 1H anatomical images. BaSSI demonstrated superior detection sensitivity in the benchmarking analysis, achieving Rc values approximately twice as high as those obtained with the 3D UTE BSSFP method. The technique successfully facilitated imaging and precise localization of 19F hotspots in postmortem experiments. However, it is important to highlight that imaging 10 mM PFOB in small mice postmortem, utilizing a 48 × 48 × 48 3D scan, demanded a substantial scan time of 1 h and 45 min. Further studies will explore accelerated imaging techniques, such as compressed sensing, to enhance BaSSI's clinical utility. [ABSTRACT FROM AUTHOR]

Background: Conventional segmented, retrospectively gated cine (Conv‐cine) is challenged in patients with breath‐hold difficulties. Compressed sensing (CS) has shown values in cine imaging but generally requires long reconstruction time. Recent artificial intelligence (AI) has demonstrated potential in fast cine imaging. Purpose: To compare CS‐cine and AI‐cine with Conv‐cine in quantitative biventricular functions, image quality, and reconstruction time. Study Type: Prospective human studies. Subjects: 70 patients (age, 39 ± 15 years, 54.3% male). Field Strength/Sequence: 3T; balanced steady state free precession gradient echo sequences. Assessment: Biventricular functional parameters of CS‐, AI‐, and Conv‐cine were measured by two radiologists independently and compared. The scan and reconstruction time were recorded. Subjective scores of image quality were compared by three radiologists. Statistical Tests: Paired t‐test and two related‐samples Wilcoxon sign test were used to compare biventricular functional parameters between CS‐, AI‐, and Conv‐cine. Intraclass correlation coefficient (ICC), Bland–Altman analysis, and Kendall's W method were applied to evaluate agreement of biventricular functional parameters and image quality of these three sequences. A P‐value <0.05 was considered statistically significant, and standardized mean difference (SMD) < 0. 100 was considered no significant difference. Results: Compared to Conv‐cine, no statistically significant differences were identified in CS‐ and AI‐cine function results (all P > 0.05), except for very small differences in left ventricle end‐diastole volumes of 2.5 mL (SMD = 0.082) and 4.1 mL (SMD = 0.096), respectively. Bland–Altman scatter plots revealed that biventricular function results were mostly distributed within the 95% confidence interval. All parameters had acceptable to excellent interobserver agreements (ICC: 0.748–0.989). Compared with Conv‐cine (84 ± 13 sec), both CS (14 ± 2 sec) and AI (15 ± 2 sec) techniques reduced scan time. Compared with CS‐cine (304 ± 17 sec), AI‐cine (24 ± 4 sec) reduced reconstruction time. CS‐cine demonstrated significantly lower quality scores than Conv‐cine, while AI‐cine demonstrated similar scores (P = 0.634). Conclusion: CS‐ and AI‐cine can achieve whole‐heart cardiac cine imaging in a single breath‐hold. Both CS‐ and AI‐cine have the potential to supplement the gold standard Conv‐cine in studying biventricular functions and benefit patients having difficulties with breath‐holds. Level of Evidence: 1 Technical Efficacy Stage: 1 [ABSTRACT FROM AUTHOR]

Codebooks with small cross-correlation amplitude have extensive applications in many fields including code division multiple access (CDMA) communications systems, space-time codes, and compressed sensing. In this paper, a class of asymptotically optimal codebooks was constructed, and the maximum inner-product of these presented codebooks was determined by using properties of character sums over finite fields. Furthermore, these codebooks provided new parameters. [ABSTRACT FROM AUTHOR]

Purpose: Time‐lapse MRI enables tracking of single iron‐labeled cells. Yet, due to temporal blurring, only slowly moving cells can be resolved. To study faster cells for example during inflammatory processes, accelerated acquisition is needed. Methods: A rotating phantom system was developed to quantitatively measure the current maximum detectable speed of cells in time‐lapse MRI. For accelerated cell tracking, an interleaved radial acquisition scheme was applied to phantom and murine brain in vivo time‐lapse MRI experiments at 9.4 T. Detection of iron‐labeled cells was evaluated in fully sampled and undersampled reconstructions with and without compressed sensing. Results: The rotating phantom system enabled ultra‐slow rotation of phantoms and a velocity detection limit of full‐brain Cartesian time‐lapse MRI of up to 172 μm/min was determined. Both phantom and in vivo measurements showed that single cells can be followed dynamically using radial time‐lapse MRI. Higher temporal resolution of undersampled reconstructions reduced geometric distortion, the velocity detection limit was increased to 1.1 mm/min in vitro, and previously hidden fast‐moving cells were recovered. In the mouse brain after in vivo labeling, a total of 42 ± 4 cells were counted in fully sampled, but only 7 ± 1 in undersampled images due to streaking artifacts. Using compressed sensing 33 ± 4 cells were detected. Conclusion: Interleaved radial time‐lapse MRI permits retrospective reconstruction of both fully sampled and accelerated images, enables single cell tracking at higher temporal resolution and recovers cells hidden before due to blurring. The velocity detection limit as determined with the rotating phantom system increased two‐ to three‐fold compared to previous results. [ABSTRACT FROM AUTHOR]

Image reconstruction is a crucial aspect of computational imaging. The compressed sensing reconstruction (CS) method has been developed to obtain high-quality images. However, the CS method is commonly time-consuming in image reconstruction. To overcome this drawback, we propose a compressed sensing reconstruction method with fast convolution filtering (F-CS method), which significantly increases reconstruction speed by reducing the number of convolution operations without image fill. The experimental results show that by using the F-CS method, the reconstruction speed can be increased by a factor of 7 compared to the conventional CS method. Moreover, the F-CS method proposed in this paper is compared with the back-propagation reconstruction (BP) method and super-resolution reconstruction (SR) method, and it is validated that the proposed method has a lower computational resource cost for high-quality image reconstruction and exhibits a much more balanced capability. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations. [ABSTRACT FROM AUTHOR]

Challenge is to find the support vectors of the unknown block sparse vector with compressed measurements in an underdetermined system where the number of unknowns is more than that of measurements. To recover unknown block sparse vector, restricted isometry property (RIP) is a sufficient condition need to be satisfied. Finding the restricted isometric constant is a non-polynomial hard problem for large values of n. In this paper coherence-based recovery guarantee has been proposed to recover the support vectors using block generalized orthogonal matching pursuit (BGOMP). It is proved that BGOMP can able to recover the support vectors with lesser number of iteration than block orthogonal matching pursuit (BOMP) by selecting multiple block support elements per iteration. Simulation results show detection performance of BGOMP is better than BOMP, block subspace pursuit (BSP) and block compressive sampling matching pursuit (BCoSaMP) for different block sparsity and block length. In most of the cases for different block sparsity and block length computation time for BGOMP is lesser than BCoSaMP, BSP and BOMP due to the multiple selection of elements in each iteration. [ABSTRACT FROM AUTHOR]

When investigating the vortex-induced vibration (VIV) of marine risers, extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers. The problem is conventionally solved using the modal decomposition method, based on the principle that the response can be approximated by a weighted sum of limited vibration modes. However, the method is not valid when the problem is underdetermined, i.e., the number of unknown mode weights is more than the number of known measurements. This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit (CoSaMP) algorithm, exploiting the sparsity of VIV in the modal space. In the validation study based on high-order VIV experiment data, the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed. A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem, which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of breath-hold (BH) high-resolution (HR) T1-weighted gradient echo hepatobiliary phase (HBP) imaging using compressed sensing (CS) in gadoxetic acid-enhanced liver MRI in comparison with standard HBP imaging using parallel imaging (PI). Methods: The study included 122 patients with liver tumors with hypointensity in the HBP who underwent both HR HBP imaging with CS and standard HBP imaging with PI. Two radiologists evaluated the liver edge sharpness, hepatic vessel conspicuity, bile duct conspicuity, image noise, and overall image quality, as well as the lesion conspicuity on HR and standard HBP imaging and the contrast-enhanced (CE) MR cholangiography (MRC) image quality reconstructed from HBP images. As a quantitative analysis, the SNR of the liver and the liver to lesion signal intensity ratio (LLSIR) were also determined. Results: The liver edge sharpness, hepatic vessel conspicuity, bile duct conspicuity, and overall image quality as well as the lesion conspicuity and the LLSIR on HR HBP imaging with CS were significantly higher than those on standard HBP imaging (all of P < 0.001). The image quality of CE-MRC reconstructed from HR HBP imaging with CS was also significantly higher than that from standard HBP imaging (P < 0.001). Conversely, the SNR of liver in standard HBP was significantly higher than that in HR HBP with CS (P < 0.001). Conclusion: BH HR HBP imaging with CS provided an improved overall image quality, lesion conspicuity, and CE-MRC visualization when compared with standard HBP imaging without extending the acquisition time. [ABSTRACT FROM AUTHOR]

IntroductionHigh-resolution whole-heart coronary magnetic resonance angiography (CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration highly desirable. Traditional reconstruction methods used in CMRA rely on either hand-crafted priors or supervised learning models. Although the latter often yield superior reconstruction quality, they require a large amount of training data and memory resources, and may encounter generalization issues when dealing with out-of-distribution datasets.MethodsTo address these challenges, we introduce an unsupervised reconstruction method that combines deep image prior (DIP) with compressed sensing (CS) to accelerate 3D CMRA. This method incorporates a slice-by-slice DIP reconstruction and 3D total variation (TV) regularization, enabling high-quality reconstruction under a significant acceleration while enforcing continuity in the slice direction. We evaluated our method by comparing it to iterative SENSE, CS-TV, CS-wavelet, and other DIP-based variants, using both retrospectively and prospectively undersampled datasets.ResultsThe results demonstrate the superiority of our 3D DIP-CS approach, which improved the reconstruction accuracy relative to the other approaches across both datasets. Ablation studies further reveal the benefits of combining DIP with 3D TV regularization, which leads to significant improvements of image quality over pure DIP-based methods. Evaluation of vessel sharpness and image quality scores shows that DIP-CS improves the quality of reformatted coronary arteries.DiscussionThe proposed method enables scan-specific reconstruction of high-quality 3D CMRA from a five-minute acquisition, without relying on fully-sampled training data or placing a heavy burden on memory resources.

The article aims to address the challenges of information degradation and distortion in graphic design, focusing on optimizing the traditional compressed sensing (CS) model. This optimization involves creating a co-reconstruction group derived from compressed observations of local image blocks. Following an initial reconstruction of compressed observations within similar groups, an initially reconstructed image block co-reconstruction group is obtained, featuring degraded reconstructed images. These images undergo channel stitching and are input into a global residual network. This network is composed of a non-local feature adaptive interaction module stacked with the aim of fusion to enhance local feature reconstruction. Results indicate that the solution space constraint for reconstructed images is achieved at a low sampling rate. Moreover, high-frequency information within the images is effectively reconstructed, improving image reconstruction accuracy.

The performance of most existing tomographic synthetic aperture radar (SAR) (TomoSAR) methods that reconstruct the scene pixel-by-pixel is degraded by speckle noise and low signal-to-noise ratio. To solve these problems, we propose a homogeneous region-based compressed sensing (HomoR-CS) method for SAR tomography. This method enhances the processing approach by transitioning from the traditional pixel-by-pixel processing to the joint processing based on multiple adjacent pixels in homogeneous regions. First, a similarity measure is designed to cluster pixels that have similar electromagnetic scattering intensities and three-dimensional (3-D) positions into homogeneous regions. Then, the homogeneous region-oriented TomoSAR signal model is proposed and the solution is given. Sparse approximation at the homogeneous region level is used to estimate the elevation for every pixel. The multiple adjacent pixels within the homogeneous region are jointly processed to determine the initial elevation centers, which are used as the prior information for the subsequent accurate elevation estimation of each pixel. Finally, the experimental analyses using both simulated and measured data validate the effectiveness of the HomoR-CS method. The results show that the HomoR-CS method reduces the outliers of 3-D reconstruction results and enhances the recovery of weak scattering targets.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

PURPOSE: To improve image quality and resolution of dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) by developing acquisition and reconstruction methods exploiting the temporal regularity property of DSC-PWI signal. THEORY AND METHODS: A novel regularized reconstruction is proposed that recovers DSC-PWI series from interleaved segmented spiral k-space acquisition using higher order temporal smoothness (HOTS) properties of the DSC-PWI signal. The HOTS regularization is designed to tackle representational insufficiency of the standard first-order temporal regularizations for supporting higher accelerations. The higher accelerations allow for k-space coverage with shorter spiral interleaves resulting in improved acquisition point spread function, and acquisition of images at multiple TEs for more accurate DSC-PWI analysis. RESULTS: The methods were evaluated in simulated and in-vivo studies. HOTS regularization provided increasingly more accurate models for DSC-PWI than the standard first-order methods with either quadratic or robust norms at the expense of increased noise. HOTS DSC-PWI optimized for noise and accuracy demonstrated significant advantages over both spiral DSC-PWI without temporal regularization and traditional echo-planar DSC-PWI, improving resolution and mitigating image artifacts associated with long readout, including blurring and geometric distortions. In context of multi-echo DSC-PWI, the novel methods allowed ∼4.3× decrease of voxel volume, providing 2× number of TEs compared to the previously published results. CONCLUSIONS: Proposed HOTS reconstruction combined with dynamic spiral sampling represents a valid mechanism for improving image quality and resolution of DSC-PWI significantly beyond those available with established fast imaging techniques.

Abstract Beamspace processing is widely applied in Direction‐of‐Arrival (DOA) estimation thanks to dimensional reduction and super‐resolution characterisations. However, the conventional atomic norm minimisation (ANM) based methods for beamspace DOA estimation are of high computational complexity for large arrays. To deal with this issue, the proposed method focuses on locating the sources in the mainlobe of the beamspace and encodes such prior information into the ANM problem without frequency‐selective constraints. The proposed method approximates the beamspace array manifold in the mainlobe sector with the truncated sector Fourier series. The theoretical analysis shows that such approximation with the properly designed fitting error relaxation on the boundary yields the low dimensional semidefinite programming (SDP) approximate implementation of the proposed ANM method and guarantees the support recovery inside the mainlobe. Furthermore, the low complexity Burer‐Monteiro factorisation based alternating direction method of multipliers method is proposed to solve the SDP problem. The complexity analysis and simulations show that the proposed method results in significant computational complexity reduction and slightly better performance compared with state‐of‐art benchmarks.

Abstract Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

Abstract Background This study aimed to determine the optimal scan time for high temporal resolution magnetic resonance (MR) imaging of the liver with gadoxetate disodium injection in combination with compressed sensing (CS) and parallel imaging (PI) techniques under single breath-holding using a 1.5-T MR system. Methods Sixty-two participants underwent multiple arterial phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the liver with gadoxetate disodium using fat-suppressed GRE T1-weighted imaging—liver acquisition with volume acceleration (LAVA)—in combination with CS and PI using a 1.5-T MR system. Forty-six and 22 participants underwent 6-s and 10-s scans, respectively. Pre-contrast, multiple arterial, portal venous, and hepatobiliary phase images were acquired. Two radiologists evaluated the visual scores for the outline of the liver, inferior right hepatic vein (IRHV), right portal vein, right hepatic artery, appropriateness of the arterial phase, and overall image quality using a 4- or 5-point scale. Results The overall image quality and the image quality of the outline of the liver in the pre-contrast and arterial phases and IRHV in the pre-contrast phase were significantly better (P

Abstract 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.

Abstract Sparsity finds applications in diverse areas such as statistics, machine learning, and signal processing. Computations over sparse structures are less complex compared to their dense counterparts and need less storage. This paper proposes a heuristic method for retrieving sparse approximate solutions of optimization problems via minimizing the $$\ell _{p}$$ ℓ p quasi-norm, where $$0

Abstract When using untrusted third parties to compress and transmit images in real‐life scenarios, it is vital to encrypt them before compression. In order to better address the issues of low security in the original image and poor reconstruction quality of the encrypted image during compressed sensing, this paper proposes a 2D compressed sensing scheme for encrypted images based on complex‐valued measurement matrix (2DCS‐CVM). Firstly, the SHA‐256 algorithm generates keys for the hyperchaotic Lorenz system, and then the chaotic sequences are used to create encrypted images with increased security through subtractive diffusion and global permutation. Secondly, the complex‐valued Vandermonde measurement matrix is utilized for 2D compressed sensing on the encrypted image, and the two‐dimensional projected gradient with embedding decryption algorithm is used to generate recovered images with improved reconstruction performance. Finally, the measurement matrix's computational complexity and transmission bandwidth are reduced through structural sparsification with sparse random matrices. Simulation results demonstrate that this scheme offers an optimal balance between storage, computational complexity, hardware implementation, and reconstruction performance while providing excellent security and robustness.

This research paper explores the rising interest in utilising deep learning methods, specifically Convolution Neural Network (CNN), to improve the reconstruction of Compressively Sampled Magnetic Resonance Imaging (CS MRI) images from under-sampled data. By training deep learning architectures on extensive data sets of paired under sampled and fully sampled MR images, these models aim to capture intricate patterns and structures, ultimately enhancing the accuracy of MR image reconstructions. A novel deep learning model is proposed dubbed as “Deep Iterative Shrinkage Thresholding Algorithm-Compressed Sensing Network” (DISTA-CSNet), specifically designed for efficient recovery of CS MRI. Our model showcases impressive results with only 20 epochs and can be effortlessly trained on diverse datasets. To ensure robustness across different datasets, the dropouts are incorporated into the model, evident from testing results. The trained DISTA-CSNet exhibits remarkable performance in recovering CS MRI from various data sets, surpassing several advanced deep learning techniques with changing compression ratios consistently. The proposed model demonstrates significant enhancements in both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, affirming the efficacy of our proposed model. The capability of our DISTA-CSNet in accurately reconstructing CS MRI images from 5-fold undersampled data shows promise in improving medical imaging applications and advancing the field of compressed sensing MRI.

This study investigates a continuous-time method for sparse signal recovery, which is suitable for analog optical circuit implementation. The proposed method is defined by a nonlinear ordinary differential equation (ODE) derived from the gradient flow dynamics of the Lasso objective function. Numerical experiments show that the proposed method certainly finds original sparse vectors within reasonable accuracy. To gain insight into the local convergence properties of the proposed method, a linear approximation around the equilibrium point is applied, yielding a closed-form error evolution ODE. This analysis shows the behavior of convergence to the equilibrium point. In addition, a variational optimization problem is proposed to optimize a time-dependent regularization parameter in order to improve both convergence speed and solution quality. The deep unfolded variational optimization method is introduced as a means of solving this optimization problem, and its effectiveness is validated through numerical experiments.

The development of measurement matrices remains a pivotal focus within the domain of compressed sensing theory. This paper introduces an innovative methodology for the construction of a deterministic binary measurement matrix, harnessing the properties of chaotic sequences. This approach streamlines the process and only requires a fixed number of initial variables for matrix construction. The construction method of the measurement matrix is divided into two distinct stages. Initially, the matrix elements are extracted through gapless sampling of chaotic sequences. Recognizing the inherent correlation among chaotic sequence elements due to gapless sampling, this paper introduces a novel nonlinear binary transformation. This innovative approach effectively mitigates the correlation and concurrently addresses storage limitations. Thereafter, the entries are integrated into a Toeplitz matrix, leveraging its structured row and columnar properties to augment storage efficiency. In subsequent steps, the approach integrates the Householder transformation to generate an orthogonal basis, thereby enhancing the matrix’s performance. Additionally, an XOR operation is employed to optimize the distribution of entries across each column vector. The research substantiates that the resultant matrix exhibits low correlation, is facile in generation, and amenable to reconstruction. Comprehensive experimental data corroborate the efficacy of the constructed measurement matrix in processing both one-dimensional and two-dimensional signals. The matrix demonstrates a strong potential to meet the Restricted Isometry Property (RIP), thereby underscoring its substantial advantages across a myriad of considerations.

Blade tip-timing (BTT) is a direct blade vibration monitoring technique and how to use under-sampled BTT signals for blade damage detection is still challenging under variable conditions. Compressed sensing (CS) has been introduced for reconstructing BTT vibration signals, but classical CS reconstruction algorithms are limited by the sparsity and slow optimization process. In order to overcome this issue, this paper presents an iteratively learning reconstruction method by introducing the vector approximate message passing (VAMP) algorithm, called VampNet. Firstly, a discrete Multi-coset sampling (MCS)-based CS model is built for BTT vibration signals in order domain and an improved Hanning-Possion window is integrated to reduce the order spectrum leakage in discretization. Then the VampNet model is proposed to reconstruct blade vibration engine orders (EOs) and the sensing matrix is discussed. Based on reconstructed vibration EOs, a cointegration-based method is proposed for blade damage detection, so that the influences of variable conditions can be reduced. Finally, the feasibility of the proposed method is testified by Matlab simulations and experimental dataset. The results show that blade vibration EOs can be accurately reconstructed by the VampNet and then small cracks can be detected by using the cointegrating residual.

The cumulative coherence condition serves as a valuable tool within the realm of Compressed Sensing. However, its application to the $\ell _{1}-\ell _{2}$ -minimization model lacks thorough discussion. This paper aims to address this gap by introducing a sufficient condition for stable recovery of sparse vectors under the $\ell _{1}-\ell _{2}$ -minimization model, building upon the cumulative coherence condition. Moreover, employing graphical analysis, this study contrasts our proposed sufficient condition with existing criteria for stable recovery using both $\ell _{1}-\ell _{2}$ -minimization and $\ell _{1}$ -minimization models. Experimental data illustrate that our proposed sufficiency conditions exhibit less stringent requirements compared to established conclusions.

In compressed sensing (CS), coherence is a simple and practical measure of the quality of a sensing matrix. The smaller the coherence of the sensing matrix, the better the reconstruction result. Most strategies for optimizing the coherence of a sensing matrix are only applicable to gray-value matrices and are not to binary matrices with fast computation speed and smaller storage space. Under a certain condition, the coherence can be improved by weighting the sensing matrix to make its condition number equal to 1, i.e., all non-zero singular values become the same. In this paper, we propose a method for reweighting CS models to reduce the condition number of the sensing matrix so as to improve its coherence, prove that the condition number decreases monotonically to 1 as the weighting times approaches infinity, and then obtain a coherence improvement model equivalent to the original CS model. Finally, we give the RwOMP recovery algorithm based on the proposed reweighted method and verify its superiority by using different binary sensing matrices for CS experiments.

This paper presents a comparison between compressed sensing (CS) and auto-encoder (AE) for compression and restoration of signals. The study used K-sparse vectors and generated an under-determined system, which is a system of linear equations with fewer equations than unknowns. By using CS and AE under various specific conditions, the accuracy of the signal restoration is compared with mean squared error (MSE). The experimental methodology includes comparing and analyzing the signal recovery performance by altering the algorithm and various parameters. The result represents the performance and accuracy of signal compression and restoration obtained using both techniques. It also provides a comprehensive analysis of CS and AE methods. The importance of this research and the possibility of practical application in various fields are discussed. Overall, this study provides insights into the comparison of CS and AE techniques in the field of sparse signal compression and restoration.

Abstract With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage space. Although the existing research has proposed compression methods for multivariate time series (MTS), it is still a challenge to guarantee the correlation between data when compressing the MTS. This paper proposes an MTS Collaborative Compression (MTSCC) method based on the two-step compression scheme. First, shape-based clustering is implemented to group the MTS. Afterward, the compressed sensing is optimized to achieve collaborative compression of grouped data. Based on a real-world MTS dataset, the experimental results show that the proposed MTSCC can effectively preserve the complex temporal correlation between indicators while achieving efficient data compression, and the root mean squared error of correlation between the reconstructed and original data is only 0.0489 in the case of 30% compression ratio. Besides, it is verified that using the reconstructed data in the production environment has almost the same performance as using the original data.

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.

The image may be maliciously tampered when transmitted on the shared platform, and the receiver cannot determine whether the received image is a true image. In order to solve the above problems, an image encryption and authentication scheme with visual security based on compressed sensing and double random phase encoding are proposed. Firstly, the plain image is compressed and encoded by double random phase encoding to obtain authentication information to verify the authenticity of the image. Then, the compressed image and authentication information are fused and then scrambled to hide the authentication information and ensure the security of the encrypted image and authentication information. Finally, the secret image and authentication information are embedded into the carrier image by singular value decomposition and least significant bit method, respectively, to improve the security of the secret image and the anti-attack ability of the authentication information. The experimental results show that the PSNR and peak-to-correlation energy of the image are still greater than 17 dB and 0.001 under a certain attack intensity, indicating that the scheme has good reconstruction quality and strong robustness. [ABSTRACT FROM AUTHOR]

Medical images contain sensitive information that will be shared with healthcare workers, patients, and researchers for diagnostic and research purposes. However, the security of these images during transmission is extremely important, especially after the COVID-19 pandemic. In diagnosis, MRI and CT images are two common medical images, and the two medical images complement each other to a certain extent. The fusion of these two images can help doctors diagnose more intuitively. This paper proposes a zero-watermarking encryption scheme with an authentication function based on multimodal medical image fusion. In addition, an improved three-dimensional Chebyshev and ICMIC closed-loop coupling mapping (3D-CICCM) is proposed and applied to the design of medical image encryption. This scheme mainly includes: medical image fusion based on bilateral filtering and local gradient energy, authentication based on the Fresnel transform, double random phase encryption based on the Fresnel transform and shuffle algorithm, and zero-watermark generation algorithm based on stationary wavelet transform. The experimental results prove the effectiveness of this scheme. [ABSTRACT FROM AUTHOR]

To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what differs from other unfolded networks is that ADMM-1DNet learns a redundant analysis operator, which can reduce the impact of outdoor high noise on reconstruction error by improving the signal sparse level. The implementation scheme includes the field operation of mechanical equipment and the operation of the data center. The empirical network trained by the local data center conducts an online reconstruction of the received outdoor vibration signal data. Experiments are conducted on two open-source bearing datasets, which verify that the proposed method outperforms the baseline method in terms of reconstruction accuracy and feature preservation, and the proposed implementation scheme can be adapted to the needs of different types of vibration signal reconstruction tasks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the graph signals are sparse in the graph Fourier domain and propose an iterative threshold compressed sensing reconstruction (ITCSR) algorithm to reconstruct sparse graph signals in the graph Fourier domain. The proposed ITCSR algorithm derives from the well-known compressed sensing by considering a threshold for sparsity-promoting reconstruction of the underlying graph signals. The proposed ITCSR algorithm enhances the performance of sparse graph signal reconstruction by introducing a threshold function to determine a suitable threshold. Furthermore, we demonstrate that the suitable parameters for the threshold can be automatically determined by leveraging the sparrow search algorithm. Moreover, we analytically prove the convergence property of the proposed ITCSR algorithm. In the experimental, numerical tests with synthetic as well as 3D point cloud data demonstrate the merits of the proposed ITCSR algorithm relative to the baseline algorithms. [ABSTRACT FROM AUTHOR]

Traditional single-pixel imaging (SPI) encounters challenges such as high sampling redundancy and poor imaging quality, constraining its widespread application. Despite a range of orthogonal modulation modes have been employed in structured illumination to enhance imaging performance, some encoding issues still persist in information sampling, impeding the further progression of SPI. We propose an SPI method based on orthogonal Hermite–Gaussian (HG) moments, achieving improved imaging reconstruction through differential modulation of HG basis patterns and linear weighting of acquired intensity. Both simulations and experiments confirm superior imaging quality and computation efficiency of proposed Hermite–Gaussian single-pixel imaging (HG-SI), especially at low-measurement levels. Moreover, we incorporate compressed sensing algorithms within the framework of HG-SI, integrating moments-based sampling strategies to optimize imaging capability under sparse measurements. Our research underscores the effectiveness of HG modulation in SPI reconstruction, enabling high-quality outcomes via compressed sampling. This advancement propels the investigation of optical field modulation modes within SPI and holds promise in offering a universal solution for weak-intensity and non-visible light microscopy. [ABSTRACT FROM AUTHOR]

Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection. [ABSTRACT FROM AUTHOR]

Numerous attempts have been made to develop efficient methods for solving the system of constrained nonlinear equations due to its widespread use in diverse engineering applications. In this article, we present a family of inertial‐based derivative‐free projection methods with a correction step for solving such system, in which the selection of the derivative‐free search direction is flexible. This family does not require the computation of corresponding Jacobian matrix or approximate matrix at every iteration and possess the following theoretical properties: (i) the inertial‐based corrected direction framework always automatically satisfies the sufficient descent and trust region properties without specific search directions, and is independent of any line search; (ii) the global convergence of the proposed family is proven under a weaker monotonicity condition on the mapping E$$ E $$, without the typical monotonicity or pseudo‐monotonicity assumption; (iii) the results about convergence rate of the proposed family are established under slightly stronger assumptions. Furthermore, we propose two effective inertial‐based derivative‐free projection methods, each embedding a specific search direction into the proposed family. We present preliminary numerical experiments on certain test problems to demonstrate the effectiveness and superiority of the proposed methods in comparison with existing ones. Additionally, we utilize these methods for solving sparse signal restorations and image restorations in compressive sensing applications. [ABSTRACT FROM AUTHOR]

We introduce a lifted |$\ell _1$| (LL1) regularization framework for the recovery of sparse signals. The proposed LL1 regularization is a generalization of several popular regularization methods in the field and is motivated by recent advancements in re-weighted |$\ell _1$| approaches for sparse recovery. Through a comprehensive analysis of the relationships between existing methods, we identify two distinct types of lifting functions that guarantee equivalence to the |$\ell _0$| minimization problem, which is a key objective in sparse signal recovery. To solve the LL1 regularization problem, we propose an algorithm based on the alternating direction method of multipliers and provide proof of convergence for the unconstrained formulation. Our experiments demonstrate the improved performance of the LL1 regularization compared with state-of-the-art methods, confirming the effectiveness of our proposed framework. In conclusion, the LL1 regularization presents a promising and flexible approach to sparse signal recovery and invites further research in this area. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer‐weighted (APTw) images. Brain tumor MRI data of 40 scans were studied. Standard images using sensitivity encoding (SENSE) with an acceleration factor (AF) of 2 were used as the gold standard, and APTw images using SENSE with CS (CS‐SENSE) with an AF of 4 were assessed. Regions of interest (ROIs), including normal tissue, edema, liquefactive necrosis, and tumor, were manually drawn, and the effects of CS‐SENSE on radiomics were assessed for each ROI category. An intraclass correlation coefficient (ICC) was first calculated for each feature extracted from APTw images with SENSE and CS‐SENSE for all ROIs. Different filters were applied to the original images, and the effects of these filters on the ICCs were further compared between APTw images with SENSE and CS‐SENSE. Feature deviations were also provided for a more comprehensive evaluation of the effects of CS‐SENSE on radiomic features. The ROI‐based comparison showed that most radiomic features extracted from CS‐SENSE‐APTw images and SENSE‐APTw images had moderate or greater reliabilities (ICC ≥ 0.5) for all four ROIs and all eight image sets with different filters. Tumor showed significantly higher ICCs than normal tissue, edema, and liquefactive necrosis. Compared to the original images, filters (such as Exponential or Square) may improve the reliability of radiomic features extracted from CS‐SENSE‐APTw and SENSE‐APTw images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a projection-based hybrid spectral gradient algorithm for nonlinear equations with convex constraints, which is based on a certain line search strategy. Convex combination technique is used to design a novel spectral parameter that is inspired by some classical spectral gradient methods. The search direction also meets the sufficient descent condition and trust region feature. The global convergence of the proposed algorithm has been established under reasonable assumptions. The results of the experiment demonstrate the proposed algorithm is both more promising and robust than some similar methods, and it is also capable of handling large-scale optimization problems. Furthermore, we apply it to problems involving sparse signal recovery and blurred image restoration. [ABSTRACT FROM AUTHOR]