Comparison of In-situ Near Infrared Melt Pool Imagery to Optical Microscopy Measurements
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additive manufacturing↗
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Microtron 2018 Scintillator Study: Application of Physics-Based Deblur on High-Energy Radiographic Imaging [Slides]
Presentation to be given at the 2018 JOWOG 32HDT to be held on Sept. 10-13, 2018 at Los Alamos National Laboratory
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Deblurring
In most instances, traditional EEG methodology provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. This article describes a method called Deblurring for increasing the spatial detail of the EEG and for fusing neurophysiologic and neuroanatomic data. Deblurring estimates potentials near the outer convexity of the cortex using a realistic finite element model of the structure of a subject's head determined from their magnetic resonance images. Deblurring is not a source localization technique and thus makes no assumptions about the number or type of generator sources. The validity of Deblurring has been initially tested by comparing deblurred data with potentials measured with subdural grid recordings. Results suggest that deblurred topographic maps, registered with a subject's magnetic resonance imaging and rendered in three dimensions, provide better spatial detail than has heretofore been obtained with scalp EEG recordings. Example results are presented from research studies of somatosensory stimulation, movement, language, attention and working memory. Deblurred ictal EEG data are also presented, indicating that this technique may have future clinical application as an aid to seizure localization and surgical planning.
Non-NASA Center↗
Frontal Slice Approaches for Tensor Linear Systems
Inspired by the row and column action methods for solving large-scale linear systems, in this work, we explore the use of frontal slices for solving tensor linear systems. In particular, this paper presents a novel approach for using frontal slices of a tensor $\mathcal{A}$ to solve tensor linear systems $\mathcal{A} ∗\mathcal{X} = \mathcal{B}$ where ∗ denotes the $t$-product. In addition, we consider variations of this method, including cyclic, block, and randomized approaches, each designed to optimize performance in different operational contexts. Our primary contribution lies in the development and convergence analysis of these methods. Experimental results on synthetically generated and real-world data, including applications such as image and video deblurring, demonstrate the efficacy of our proposed approaches and validate our theoretical findings.
Luo, Hengrui↗
Sequential Image Recovery Using Joint Hierarchical Bayesian Learning
Abstract Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery of the individual images. Although some recent (variational) methods have demonstrated high-resolution image recovery based on jointly recovering sequential images, there remain robustness issues due to parameter tuning and restrictions on the type of sequential images. Here, we present a method based on hierarchical Bayesian learning for the joint recovery of sequential images that incorporates prior intra- and inter-image information. Our method restores the missing information in each image by “borrowing” it from the other images. More precisely, we couple sequential images by penalizing their pixel-wise difference. The corresponding penalty terms (one for each pixel and pair of subsequent images) are treated as weakly-informative random variables that favor small pixel-wise differences but allow occasional outliers. As a result, all of the individual reconstructions yield improved accuracy. Our method can be used for various data acquisitions and allows for uncertainty quantification. Some preliminary results indicate its potential use for sequential deblurring and magnetic resonance imaging.
Xiao, Yao↗
Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification
Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.
36 MATERIALS SCIENCE↗
Combining Poisson singular integral and total variation prior models in image restoration
In this study, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation(TV) image prior preserves edge structures while imposing smoothness on the solutions.However,it tends to oversmooth textured areas.To alleviate this problem we propose to combine the TV and the Poisson Singular Integral(PSI)models, which,as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle,and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach,in the experimental section we compare it with other restoration methods.
97 MATHEMATICS AND COMPUTING↗
Evaluation of Deblur Methods for Radiography
Radiography is used as a primary diagnostic for dynamic experiments, providing timeresolved radiographic measurements of areal mass density along a line of sight through the experiment. It is well known that the finite spot extent of the radiographic source, as well as scattering, are sources of blurring of the radiographic images. This blurring interferes with quantitative measurement of the areal mass density. In order to improve the quantitative utility of this diagnostic, it is necessary to deblur or “restore” the radiographs to recover the “true” areal mass density from a radiographic transmission measurement. Towards this end, I am evaluating three separate methods currently in use for deblurring radiographs. I begin by briefly describing the problems associated with image restoration, and outlining the three methods. Next, I illustrate how blurring affects the quantitative measurements using radiographs. I then present the results of the various deblur methods, evaluating each according to several criteria. After I have summarized the results of the evaluation, I give a detailed account of how the restoration process is actually implemented.
97 MATHEMATICS AND COMPUTING↗
WE-G-204-04: Focal Spot Deblurring For High Resolution Amorphous Selenium (aSe) Complementary Metal Oxide Semiconductor (CMOS) X-Ray Detector
Purpose: CMOS-based aSe detectors compared to CsI-TFT-based flat panels have the advantages of higher spatial sampling due to smaller pixel size and decreased blurring characteristic of direct rather than indirect detection. For systems with such detectors, the limiting factor degrading image resolution then becomes the focal-spot geometric unsharpness. This effect can seriously limit the use of such detectors in areas such as cone beam computed tomography, clinical fluoroscopy and angiography. In this work a technique to remove the effect of focal-spot blur is presented for a simulated aSe detector. Method: To simulate images from an aSe detector affected with focal-spot blur, first a set of high-resolution images of a stent (FRED from Microvention, Inc.) were acquired using a 75µm pixel size Dexela-Perkin-Elmer detector and averaged to reduce quantum noise. Then the averaged image was blurred with a known Gaussian blur at two different magnifications to simulate an idealized focal spot. The blurred images were then deconvolved with a set of different Gaussian blurs to remove the effect of focal-spot blurring using a threshold-based, inverse-filtering method. Results: The blur was removed by deconvolving the images using a set of Gaussian functions for both magnifications. Selecting the correct function resulted in an image close to the original; however, selection of too wide a function would cause severe artifacts. Conclusion: Experimentally, focal-spot blur at different magnifications can be measured using a pin hole with a high resolution detector. This spread function can be used to deblur the input images that are acquired at corresponding magnifications to correct for the focal spot blur. For CBCT applications, the magnification of specific objects can be obtained using initial reconstructions then corrected for focal-spot blurring to improve resolution. Similarly, if object magnification can be determined such correction may be applied in fluoroscopy and angiography.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Scatter and Blur Corrections for High-Energy X-Ray Radiography
High-energy X-ray radiography is useful as a highly penetrating method for imaging through dense materials. However, the primary modes of interaction of X-rays at these energies involve scattering or the production of secondary high-energy photons, which can interfere with the image. In addition, detector blurring, often resulting from scatter within the detector, can reduce image sharpness. Both of these processes can be mitigated with the use of convolution kernels, with the main challenge being that the proper kernel to use is not known, particularly for the scatter contribution. By radiographing solid slabs of uniform attenuation, we show that point spread functions and material-specific point scatter functions can be determined to significantly reduce the effect of detector blurring and object scatter. Constraining the fits to the slabs and uniform transmission within the slabs is sufficient to recover these functions. A functional form that reproduces the angular distribution of high-energy bremsstrahlung X-rays is presented for recovering point scatter functions. In conclusion, the method is applied to radiographs of objects from bremsstrahlung X-ray sources operating at 4- and 7.5-MV endpoint energies and a significant increase in sharpness is observed.
Blind deconvolution↗
CodEx: A Modular Framework for Joint Temporal De-Blurring and Tomographic Reconstruction
In many computed tomography (CT) imaging applications, it is important to rapidly collect data from an object that is moving or changing with time. Tomographic acquisition is generally assumed to be step-and-shoot, where the object is rotated to each desired angle, and a view is taken. However, step-and-shoot acquisition is slow and can waste photons, so in practice fly-scanning is done where the object is continuously rotated while collecting data. However, this can result in motion-blurred views and consequently reconstructions with severe motion artifacts. In this paper, we introduce CodEx, a modular framework for joint de-blurring and tomographic reconstruction that can effectively invert the motion blur introduced in sparse view fly-scanning. The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm. CodEx works by encoding the acquisition with a known binary code that the reconstruction algorithm then inverts. Using a well chosen binary code to encode the measurements can improve the accuracy of the inversion process. The CodEx reconstruction method uses the alternating direction method of multipliers (ADMM) to split the inverse problem into iterative deblurring and reconstruction sub-problems, making reconstruction practical to implement. Here we present reconstruction results on both simulated and binned experimental data to demonstrate the effectiveness of our method.
42 ENGINEERING↗
Deconvoluting experimental decay energy spectra: The O 26 case
In nuclear reaction experiments, the measured decay energy spectra can give insights into the shell structure of decaying systems. However, extracting the underlying physics from the measurements is challenging due to detector resolution and acceptance effects. The Richardson-Lucy (RL) algorithm, a deblurring method that is commonly used in optics and has proven to be a successful technique for restoring images, was applied to our experimental nuclear physics data. The only inputs to the method are the observed energy spectrum and the detector's response matrix also known as the transfer matrix. We demonstrate that the technique can help access information about the shell structure of particle-unbound systems from the measured decay energy spectrum that is not immediately accessible via traditional approaches such as χ-square fitting. For a similar purpose, we developed a machine learning model that uses a deep neural network (DNN) classifier to identify resonance states from the measured decay energy spectrum. We tested the performance of both methods on simulated data and experimental measurements. Then, we applied both algorithms to the decay energy spectrum of 26 O → 24 O + n + n measured via invariant mass spectroscopy. Here, the resonance states restored using the RL algorithm to deblur the measured decay energy spectrum agree with those found by the DNN classifier. Both deblurring and DNN approaches suggest that the raw decay energy spectrum of 26 O exhibits three peaks at approximately 0.15 MeV, 1.50 MeV, and 5.00 MeV, with half-widths of 0.29 MeV, 0.80 MeV, and 1.85 MeV, respectively.
Spectrometers & spectroscopic techniques↗
Comparison of super-resolution and noise reduction for passive single-photon imaging
Not Available
42 ENGINEERING↗
SABER: A Systems Approach to Blur Estimation and Reduction in X-Ray Imaging
Not Available
42 ENGINEERING↗
Source function from two-particle correlation function through entropy-regularized Richardson-Lucy deblurring
Source functions are obtained from p – p and d – α correlation functions by applying the Richardson-Lucy (RL) deblurring to the Koonin-Pratt (KP) equation. To prevent fitting of noise in the correlation function, total-variation (TV) regularization is employed that has been effective in ordinary image restoration. TV alone cannot ensure normalization of the source functions. To ensure the latter, we propose a maximum-entropy regularized RL algorithm (MEM-RL). We outline the MEM-RL formalism and optimization strategy for the KP equation, demonstrating its effectiveness on both simulated and experimental data, including the p – p and d – α correlation functions.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Rapid data comparison technique development for N-ray/X-ray analysis
An operational N-ray/X-ray computer analysis system for nondestructive testing problems was realized by developing: (1) more efficient data transfer routines for use in existing image processing systems; (2) the capability to store and analyze multiple N-ray/X-ray images; (3) an efficient 2-D Fast Fourier Transform; and (4) transfer functions compensation capability to correct for distortions and nonlinearities of N-rays/X-rays. Applications of the technique are summarized for the following: thermal protection system tile with hydraulic fluid; thermal protection system tile with corrosion; thermal protection system tile debonding; stainless steel tubing corrosion; defective electrical devices; fast film restoration and deblur; corrosion tracking over time; reduction of source vignetting, and identification of materials (0 rings, Teflon parts, soft goods) via table look up.
Butterfield, R. L.↗
Phase-weighted slant stacking for surface wave dispersion measurement
SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.
58 GEOSCIENCES↗
CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys
This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
2018 NEON and 2025 CHESS Campaigns↗