Sparse Deconvolution Algorithm Research Articles

A Robust Denoised Algorithm Based on Hessian–Sparse Deconvolution for Passive Underwater Acoustic Detection

Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance. In this paper, we propose a novel two-dimensional Hessian–sparse deconvolution algorithm based on image processing techniques. This method assumes a priori that the underwater acoustic bearing time record (BTR) images exhibit sparsity, and then it first constructs partial differential equations in the beamforming domain with sparsity-norm constraints for optimal noise reduction. Subsequently, a two-dimensional deconvolution operation is applied to narrow the main lobe, aiming to achieve additional temporal gains in two-dimensional processing. The simulation and real sea trial data processing results show that the main lobe width of the proposed method is about 1.3 degrees at 0 dB. It effectively reduces the main lobe width and enhances the detection resolution of BTRs in the post-processing part, especially in low-signal-to-noise-ratio (SNR) environments. Therefore, the proposed method provides nice opportunities to further improve the target-detecting ability of hydrophone arrays.

Improved measurement of the glue layer in composite material by using sparse deconvolution.

Due to its powerful penetration, and greater spatial resolution than microwaves and ultrasonic waves, the terahertz technique stands out as being particularly useful in identifying thin glue layers in multilayered materials. However, the arrival times of echoes are challenging to pinpoint from the experimental data because of the temporal form of the incident pulse and the system noise. Here, two terahertz signal sparse deconvolution algorithms are studied to more accurately identify the times of the echoes. Using the circulant structure of the convolution matrix, the method's computation time can be lowered to hundreds of milliseconds. In addition, a method based on group velocity dispersion is investigated to reduce the impact of time-varying pulses with minimal computational expense. The presented algorithms have the potential to be employed in real-time inspection in production lines due to their quick speed and high confidence.

Single particle tracking reveals SARS-CoV-2 regulating and utilizing dynamic filopodia for viral invasion

Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry mechanism has been explored, little is known about how SARS-CoV-2 regulates the subcellular structural remodeling to invade multiple organs and cell types. Here, we unveil how SARS-CoV-2 boosts and utilizes filopodia to enter the target cells by real-time imaging. Using SARS-CoV-2 single virus-like particle (VLP) tracking in live cells and sparse deconvolution algorithm, we uncover that VLPs utilize filopodia to reach the entry site in two patterns, “surfing” and “grabbing”, which avoid the virus from randomly searching on the plasma membrane. Moreover, combining mechanical simulation, we elucidate that the formation of virus-induced filopodia and the retraction speed of filopodia depend on cytoskeleton dynamics and friction resistance at the substrate surface caused by loading-virus gravity, respectively. Further, we discover that the entry process of SARS-CoV-2 via filopodia depends on Cdc42 activity and actin-associated proteins fascin, formin, and Arp2/3. Together, our results highlight that the spatial–temporal regulation of actin cytoskeleton by SARS-CoV-2 infection makes filopodia as a highway for virus entry and potentiates it as an antiviral target.

Method for time-of-flight estimation of low frequency acoustic signals in reverberant and noisy environment with sparse impulse response

For acoustic procedures which rely on the speed of sound to derive process parameters, the determination of the acoustic time of flight (TOF) is essential. In this work, a method for the determination of the TOF is presented. It is intended for reverberant and noisy environments and can be applied in the gas holdup determination in bubbly liquids via acoustic transmission tomography for example. The method includes the selection and design of the transmitted signal to optimize the disambiguate of the autocorrelation, the narrowing of the time window based on the fractional Fourier transform to accelerate the TOF estimation. Furthermore, it includes the consideration of the system-induced signal distortion through prior quasi-anechoic measurements and the sparse reconstruction of the spatial impulse response for TOF estimation using non-negative sparse deconvolution algorithms. The method is tested analytically on numerically generated signals and various sparse deconvolution algorithms are investigated with respect to their applicability and limitations.

Resolution Improvement of Ultrasonic Signals Using Sparse Deconvolution and Variational Mode Decomposition Algorithms

Over the past few years, the use of ultrasonic arrays for nondestructive testing (NDT) applications has grown rapidly and led to use new algorithms of signal processing. The present paper’s main objective is to improve the resolution of defect detection and make the detection process as fast and accurate as possible. This paper introduces a novel method to improve the resolution of ultrasonic phased array. The proposed method is based on the variational decomposition of signal and on a deconvolution method. To reduce the level of signal’s noise, a method based on the variational mode decomposition (VMD) is used and to improve the resolution, a sparse deconvolution algorithm optimized using the majorization-minimization (MM) method is used. A simulation study has been carried out simulating a block of stainless steel containing several defects in different positions. Experimental tests were performed on a sample of stainless steel containing several defects. The obtained results show that the proposed method can improve the quality of ultrasonic data which enhances the localization of defects.

Blind deconvolution for spike inference from fluorescence recordings

The parallel developments of genetically-encoded calcium indicators and fast fluorescence imaging techniques allows one to simultaneously record neural activity of extended neuronal populations in vivo. To fully harness the potential of functional imaging, one needs to infer the sequence of action potentials from fluorescence traces. Here we build on recently proposed computational approaches to develop a blind sparse deconvolution (BSD) algorithm based on a generative model for inferring spike trains from fluorescence traces. BSD features, (1) automatic (fully unsupervised) estimation of the hyperparameters, such as spike amplitude, noise level and rise and decay time constants, (2) a novel analytical estimate of the sparsity prior, which yields enhanced robustness and computational speed with respect to existing methods, (3) automatic thresholding for binarizing spikes that maximizes the precision-recall performance, (4) super-resolution capabilities increasing the temporal resolution beyond the fluorescence signal acquisition rate. BSD also uniquely provides theoretically-grounded estimates of the expected performance of the spike reconstruction in terms of precision-recall and temporal accuracy for each recording. The performance of the algorithm is established using synthetic data and through the SpikeFinder challenge, a community-based initiative for spike-rate inference benchmarking based on a collection of joint electrophysiological and fluorescence recordings. Our method outperforms classical sparse deconvolution algorithms in terms of robustness, speed and/or accuracy and performs competitively in the SpikeFinder challenge. This algorithm is modular, easy-to-use and made freely available. Its novel features can thus be incorporated in a straightforward way into existing calcium imaging packages.

Improved superresolution microscopy imaging by sparse deconvolution with an interframe penalty

Penalized regression with a combination of sparseness and an interframe penalty is explored for image deconvolution in wide‐field single‐molecule fluorescence microscopy. The aim is to reconstruct superresolution images, which can be achieved by averaging the positions and intensities of individual fluorophores obtained from the analysis of successive frames. Sparsity of the fluorophore distribution in the spatial domain is obtained with an L0‐norm penalty on estimated fluorophore intensities, effectively constraining the number of fluorophores per frame. Simultaneously, continuity of the fluorophore localizations in the time mode is obtained by penalizing the total numbers of pixel status changes between successive frames. We implemented the interframe penalty in a sparse deconvolution algorithm (sparse image deconvolution and reconstruction) for improved imaging of densely labeled biological samples. For simulated and real biological data, we show that more accurate estimates of the final superresolution images of cellular structures can be obtained.

Sparse Deconvolution Using Support Vector Machines

Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise.

Sparse Deconvolution of B-Scan Images

In this paper, a new computationally efficient sparse deconvolution algorithm for the use on B-scan images from objects with relatively few scattering targets is presented. It is based on a linear image formation model that has been used earlier in connection with linear minimum mean squared error (MMSE) two-dimensional (2-D) deconvolution. The MMSE deconvolution results have shown improved resolution compared to synthetic aperture focusing technique (SAFT), but at the cost of increased computation time. The proposed algorithm uses the sparsity of the image, reducing the degrees of freedom in the reconstruction problem, to reduce the computation time and to improve the resolution. The dominating task in the algorithm consists in detecting the set of active scattering targets, which is done by iterating between one up-dating pass that detects new points to include in the set, and a down-dating pass that removes redundant points. In the up-date, a spatiotemporal matched filter is used to isolate potential candidates. A subset of those are chosen using a detection criterion. The amplitudes of the detected scatterers are found by MMSE. The algorithm properties are illustrated using synthetic and real B-scan. The results show excellent resolution enhancement- and noise-suppression capabilities. The involved computation times are analyzed.