SEARCH · Search NASA

Results for “Deconvolution”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Improved deconvolution of very weak confocal signals

Deconvolution is typically used to sharpen fluorescence images, but when the signal-to-noise ratio is low, the primary benefit is reduced noise and a smoother appearance of the fluorescent structures. 3D time-lapse (4D) confocal image sets can be improved by deconvolution. However, when the confocal signals are very weak, the popular Huygens deconvolution software erases fluorescent structures that are clearly visible in the raw data. We find that this problem can be avoided by prefiltering the optical sections with a Gaussian blur. Analysis of real and simulated data indicates that the Gaussian blur prefilter preserves meaningful signals while enabling removal of background noise. This approach is very simple, and it allows Huygens to be used with 4D imaging conditions that minimize photodamage .

Day, Kasey J.↗

DOE OSTI2017

Improved deconvolution of very weak confocal signals

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Fast Bayesian blind deconvolution with Huber Super Gaussian priors

We report expectation Maximization (EM) based inference has already proven to be a very powerful tool to solve blind image deconvolution (BID) problems. Unfortunately, three important problems still impede the application of EM in BID: the undesirable saddle points and local minima caused by highly nonconvex priors, the instability around zero of some of the most interesting sparsity promoting priors, and the intrinsic high computational cost of the corresponding BID algorithm. In this paper we first show how Super Gaussian priors can be made numerically tractable around zero by introducing the family of Huber Super Gaussian priors and then present a fast EM based blind deconvolution method formulated in the image space. In the proposed computational approach, image and kernel estimation are performed by using the Alternating Direction Method of Multipliers (ADMM), which allows to exploit the advantages of FFT computation. For highly nonconvex priors, we propose a Smooth ADMM (SADMM) approach to avoid poor BID estimates. In conclusion, extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art BID methods in terms of quality of the reconstructions and speed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Multiqubit noise deconvolution and characterization

We present a noise deconvolution technique for obtaining noiseless expectation values of noisy observables at the output of multiqubit quantum channels. For any number of qubits or in the presence of correlations, our protocol applies to any mathematically invertible noise model, even when its inverse map is not physically implementable, i.e., when it is neither completely positive nor trace preserving. For a generic observable affected by Pauli noise it provides a quadratic speedup, always producing a rescaling of its Pauli basis components. We show that it is still possible to achieve the deconvolution while experimentally estimating the noise parameters, whenever these are unknown (bypassing resource-heavy techniques such as quantum process tomography). We provide a simulation, with examples for both Pauli and non-Pauli channels.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Vector elastic deconvolution migration with dual wavefield decomposition

Here, we report elastic-wave imaging using multicomponent data can provide more useful subsurface information than acoustic-wave imaging, but it is usually algorithmically challenging. We have developed a vector elastic deconvolution migration method for high-resolution imaging of subsurface structures in isotropic and anisotropic elastic media. Our new method uses a vector deconvolution imaging condition based on dual wavefield decomposition, including an explicit directional wavefield separation using the Hilbert transform and a P/S vector wavefield decomposition using the low-rank decomposition method. Using three elastic models, we numerically determine that our new method produces notably higher resolution and more amplitude-balanced elastic images compared with a crosscorrelation-based vector elastic reverse time migration method.

58 GEOSCIENCES↗

DOE OSTI2021

Point-spread Function Deconvolution of the IFU Data and Restoration of Galaxy Stellar Kinematics

We present a performance test of the point-spread function (PSF) deconvolution algorithm applied to astronomical integral field unit (IFU) spectroscopy data for restoration of galaxy kinematics. We deconvolve the IFU data by applying the Lucy–Richardson algorithm to the 2D image slice at each wavelength. We demonstrate that the algorithm can effectively recover the true stellar kinematics of the galaxy, by using mock IFU data with a diverse combination of surface brightness profile, signal-to-noise ratio, line-of-sight geometry, and line-of-sight velocity distribution (LOSVD). In addition, we show that the proxy of the spin parameter ${\lambda }_{{R}_{e}}$ can be accurately measured from the deconvolved IFU data. We apply the deconvolution algorithm to the actual SDSS-IV MaNGA IFU survey data. The 2D LOSVD, geometry, and ${\lambda }_{{R}_{e}}$ measured from the deconvolved MaNGA IFU data exhibit noticeable differences compared to the ones measured from the original IFU data. The method can be applied to any other regular-grid IFU data to extract the PSF-deconvolved spatial information.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Electrospray Ionization with High-Resolution Mass Spectrometry as a Tool for Lignomics: Lignin Mass Spectrum Deconvolution

The capability to characterize lignin, lignocellulose, and their degradation products is essential for the development of new renewable feedstocks. Electrospray ionization high-resolution time-of-flight mass spectrometry (ESI-HR TOF-MS) method was developed expanding the lignomics toolkit while targeting the simultaneous detection of low and high molecular weight (MW) lignin species. The effect of a broad range of electrolytes and various ionization conditions on ion formation and ionization effectiveness was studied using a suite of mono-, di-, and triarene lignin model compounds as well as kraft alkali lignin. Contrary to the previous studies, the positive ionization mode was found to be more effective for methoxy-substituted arenes and polyphenols, i.e., species of a broadly varied MW structurally similar to the native lignin. For the first time, we report an effective formation of multiply charged species of lignin with the subsequent mass spectrum deconvolution in the presence of 100 mmol L{sup −1} formic acid in the positive ESI mode. The developed method enabled the detection of lignin species with an MW between 150 and 9000 Da or higher, depending on the mass analyzer. The obtained M{sub n} and M{sub w} values of 1500 and 2500 Da, respectively, were in good agreement with those determined by gel permeation chromatography. Furthermore, the deconvoluted ESI mass spectrum was similar to that obtained with matrix-assisted laser desorption/ionization (MALDI)-HR TOF-MS, yet featuring a higher signal-to-noise ratio. The formation of multiply charged species was confirmed with ion mobility ESI-HR Q-TOF-MS. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.

36 MATERIALS SCIENCE↗

DOE OSTI2016

TH-AB-209-03: Overcoming Resolution Limitations of Diffuse Optical Signals in X-Ray Induced Luminescence (XIL) Imaging Via Selective Plane Illumination and 2D Deconvolution

Purpose: To evaluate the resolution and sensitivity of XIL imaging using a surface radiance simulation based on optical diffusion and maximum likelihood expectation maximization (MLEM) image reconstruction. XIL imaging seeks to determine the distribution of luminescent nanophosphors, which could be used as nanodosimeters or radiosensitizers. Methods: The XIL simulation generated a homogeneous slab with optical properties similar to tissue. X-ray activated nanophosphors were placed at 1.0 cm depth in the tissue in concentrations of 10{sup −4} g/mL in two volumes of 10 mm{sup 3} with varying separations between each other. An analytical optical diffusion model determined the surface radiance from the photon distributions generated at depth in the tissue by the nanophosphors. The simulation then determined the detected luminescent signal collected with a f/1.0 aperture lens and back-illuminated EMCCD camera. The surface radiance was deconvolved using a MLEM algorithm to estimate the nanophosphors distribution and the resolution. To account for both Poisson and Gaussian noise, a shifted Poisson imaging model was used in the deconvolution. The deconvolved distributions were fitted to a Gaussian after radial averaging to measure the full width at half maximum (FWHM) and the peak to peak distance between distributions was measured to determine the resolving power. Results: Simulated surface radiances for doses from 1mGy to 100 cGy were computed. Each image was deconvolved using 1000 iterations. At 1mGy, deconvolution reduced the FWHM of the nanophosphors distribution by 65% and had a resolving power is 3.84 mm. Decreasing the dose from 100 cGy to 1 mGy increased the FWHM by 22% but allowed for a dose reduction of a factor of 1000. Conclusion: Deconvolving the detected surface radiance allows for dose reduction while maintaining the resolution of the nanophosphors. It proves to be a useful technique in overcoming the resolution limitations of diffuse optical imaging in tissue. C. S. acknowledges support from the NIH National Institute of General Medical Sciences (Award number R25GM109439, Project Title: University of Chicago Initiative for Maximizing Student Development, IMSD). B. Q. and P. L. acknowledge support from NIH grant R01EB017293.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Transient recovery problem in acoustics: A multichannel model-based deconvolution approach

Critical acoustical systems operating in complex environments contaminated with disturbances and noise offer an extreme challenge when excited by out-of-the-ordinary, impulsive, transient events that can be undetected and seriously affect their overall performance. Transient impulse excitations must be detected, extracted, and evaluated to determine any potential system damage that could have been imposed; therefore, the problem of recovering the excitation in an uncertain measurement environment becomes one of multichannel deconvolution. Recovering a transient and its initial energy has not been solved satisfactorily, especially when the measurement has been truncated and only a small segment of response data is available. The development of multichannel deconvolution techniques for both complete and incomplete excitation data is discussed, employing a model-based approach based on the state-space representation of an identified acoustical system coupled to a forward modeling solution and a Kalman-type processor for enhancement and extraction. In conclusion, synthesized data are utilized to assess the feasibility of the various approaches, demonstrating that reasonable performance can be achieved even in noisy environments.

42 ENGINEERING↗

DOE OSTI2024

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

Pandey, Vikas (ORCID:0000000154771095)↗

DOE OSTI2025

Beam Signal Recovery Using Wiener Deconvolution

This project explores the use of Wiener deconvolution to recover an original signal that has been distorted by a known transfer function and by noise. A simulated Gaussian pulse was used as the test signal, and a transfer function was applied in the frequency domain to model system distortion. Controlled noise was then introduced to approximate real-world signal degradation. A Wiener filter was implemented to reverse the effects of the transfer function while minimizing the influence of noise. The recovered signal was compared to the original pulse to evaluate the effectiveness of the filter. Results demonstrate that Wiener deconvolution offers a stable and effective approach to signal recovery, balancing complete transfer function inversion and noise suppression.

Campbell, Savanna [El Camino Coll.]↗

DOE OSTI2025

Calorimeter Pileup Deconvolution for Online Trigger Primitives

In high energy physics experiment, as the luminosity increases, pile-up issues on detectors such as calorimeters become non-negligible. Deconvolution approaches with mathematic pre-assumptions such as Sparse Representation are developed for data analysis stage. For online computation tasks such as for trigger primitive creation, signal availability is significantly different as in offline data analysis stage, and therefore, different (yet simpler) algorithms should be explored. In this document, several approaches of deconvolution suitable for FPGA implementation are discussed.

Wu, Jin-yuan [Fermilab] (ORCID:0000000344329521)↗

DOE OSTI2025

Wiener Filter Deconvolution for Analog Signals

This project explores the use of Wiener deconvolution to recover an original signal that has been distorted by a known transfer function and by noise. A simulated Gaussian pulse was used as the test signal, and a transfer function was applied in the frequency domain to model the system distortion. Controlled noise was then introduced to approximate real-world signal degradation. A Wiener filter was implemented to reverse the effects of the transfer function while minimizing the influence of noise. The recovered signal was compared with the original pulse to evaluate the effectiveness of the filter. The results demonstrate that Wiener deconvolution offers a stable and effective approach to signal recovery, balancing complete transfer function inversion and noise suppression.

Campbell, Savanna [Fermilab]↗

DOE OSTI2019

A regularized deconvolution method for turbulent closure modeling in implicitly filtered large-eddy simulation

Turbulence closure models that are based on a regularized deconvolution method (RDM) are proposed for application to implicitly filtered Large-Eddy Simulation (LES). This method reconstructs sub-grid scale contributions by approximately recovering filtered quantities through an optimization approach. Physical principles on the boundedness and conservation of reconstructed quantities are preserved by constraining the optimization problem. Further, to account for effects of scales that are not resolved in implicitly filtered LES, a reconstruction method is proposed that combines sub-grid projection and energy-similarity to enrich unresolved scales. With this method, closure models are developed for turbulent scalar fluxes and turbulence–chemistry interaction that are intrinsically consistent with the LES formulation. These closure models are evaluated in application to a piloted turbulent jet flame with inhomogeneous inlet composition, in which combustion is represented by a two-scalar manifold model. The RDM-formulation is benchmarked against simulations obtained using a presumed PDF method and eddy diffusivity closure model. In partially-premixed flame regions, it is observed that RDM improves the prediction of turbulent mixing, and faster grid convergence is observed for results with RDM compared to simulations employing a presumed PDF method.

42 ENGINEERING↗

DOE OSTI2024

Deconvoluting Effects of Lithium Morphology and SEI Stability at Moderate Current Density Using Interface Engineering

Lithium (Li)-morphology and solid electrolyte interphase (SEI) are among the most significant performance regulators in Li-metal batteries (LMBs). While both Li-morphology and SEI composition play key roles in the cyclability of LMBs, less is understood about the individual contributions of each factor to overall Li reversibility, particularly at a practical current density (1 mA cm −2 ) at which the kinetics of both factors are not naturally separated. Herein, an interface engineering approach is introduced to deconvolute the impacts of Li-morphology and SEI composition on battery performance. By using interfacial nanofilms with differing resistivity (resistive HfO 2 versus conductive ZnO), the morphology of Li is varied, and by virtue of similar acidic character of the nanofilms, the formation of anion-rich SEIs is maintained. It is established that although the surface acidity of the thin films enables preformation of a more anion-rich SEI, it is not preserved after Li plating. It is further shown that resistance-controlled, low-surface-area Li-morphology exhibits up to threefold increase in stable cycle life when tested in multiple electrolytes. Overall, these findings explain why Li-morphological control is more advantageous for performance improvement than preformed SEI modulation due to the inherent challenges in SEI preservation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution

Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of ~ 10 27 yr, requiring suppressing backgrounds to < 1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of ~ 5 when reconstructing electron-positron pairs in the 208Tl 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterráneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of ~ 10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e - e + pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗