Holograms of the aerial image of a lens.
Wave front reconstruction imaging technique for hologram production of aerial image of lens
IMAGING TECHNIQUE↗
SEARCH · Engineering Papers
Results for “image reconstruction”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Image improvement and three-dimensional reconstruction using holographic image processing
Holographic computing principles make possible image improvement and synthesis in many cases of current scientific and engineering interest. Examples are given for the improvement of resolution in electron microscopy and 3-D reconstruction in electron microscopy and X-ray crystallography, following an analysis of optical versus digital computing in such applications.
Stroke, G. W.↗
Comparison of four stable numerical methods for Abel's integral equation
The 3-D image reconstruction from cone-beam projections in computerized tomography leads naturally, in the case of radial symmetry, to the study of Abel-type integral equations. If the experimental information is obtained from measured data, on a discrete set of points, special methods are needed in order to restore continuity with respect to the data. A new combined Regularized-Adjoint-Conjugate Gradient algorithm, together with two different implementations of the Mollification Method (one based on a data filtering technique and the other on the mollification of the kernal function) and a regularization by truncation method (initially proposed for 2-D ray sample schemes and more recently extended to 3-D cone-beam image reconstruction) are extensively tested and compared for accuracy and numerical stability as functions of the level of noise in the data.
Murio, Diego A.↗
Fusing Semantic Features with Gaussian Splatting for Enhanced Satellite Image Surface Reconstruction
Explore the source record for details and available documents.
Reed, Albert William [Sandia National Laboratories↗
Surrogate Distributed Radiological Sources—Part III: Quantitative Distributed Source Reconstructions
In this third part of a multi-paper series, we present quantitative image reconstruction results from aerial measurements of eight different surrogate distributed gamma-ray sources on flat terrain. Here, we show that our quantitative imaging methods can accurately reconstruct the expected shapes, and, after appropriate calibration, the absolute activity of the distributed sources. We conduct several studies of imaging performance versus various measurement and reconstruction parameters, including detector altitude and raster pass spacing, data and modeling fidelity, and regularization type and strength. The imaging quality performance is quantified using various quantitative image quality metrics. Our results confirm the utility of point source arrays as surrogates for truly distributed radiological sources, and advance the quantitative capabilities of Scene Data Fusion gamma-ray imaging methods.
Airborne survey↗
An Image Processing Algorithm Based On FMAT
Information deleted in ways minimizing adverse effects on reconstructed images. New grey-scale generalization of medial axis transformation (MAT), called FMAT (short for Fuzzy MAT) proposed. Formulated by making natural extension to fuzzy-set theory of all definitions and conditions (e.g., characteristic function of disk, subset condition of disk, and redundancy checking) used in defining MAT of crisp set. Does not need image to have any kind of priori segmentation, and allows medial axis (and skeleton) to be fuzzy subset of input image. Resulting FMAT (consisting of maximal fuzzy disks) capable of reconstructing exactly original image.
Wang, Lui↗
An X-ray Interferometry Concept for the ESA Voyage 2050 Programme
We have proposed the development of X-ray interferometry as part of ESA’s Voyage 2050 programme, to reveal the universe at high energies with ultra-high spatial resolution. With only a 1 m baseline, which could be accommodated on a single spacecraft, X-ray interferometry can reach 100μas resolution at 10 ̊A (1.24 keV) and exceed that of the Event Horizon Telescope at 2 ̊A (6.2 keV). A multi-spacecraft ‘constellation’ interferometer would resolve well below 1μas. Here we focus on the single-spacecraft interferometer design and discuss the process of fringe detection and image reconstruction from multiple baselines, showing simulated images of testcases from our Voyage 2050 White Paper. We also discuss the challenges and feasibility of reaching the technical requirements needed for a single-spacecraft interferometer. Most key requirements are already feasible or within easy reach. Besides a ground-based testbed, covered elsewhere in these proceedings, the most important areas for development include large format, small-pixel X-ray detectors and pointing which is stable or can be reconstructed to tens of μas precision.
Phil Uttley↗
Cluster compression algorithm: A joint clustering/data compression concept
The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.
Hilbert, E. E.↗
Combustion/particle sizing experiments at the Naval Postgraduate School Combustion Research Laboratory
Particle behavior in combustion processes is an active research area at NPS. Currently, four research efforts are being conducted: (1) There is a long standing need to better understand the soot production and combustion processes in gas turbine combustors, both from a concern for improved engine life and to minimize exhaust particulates. Soot emissions are strongly effected by fuel composition and additives; (2) A more recent need for particle sizing/behavior measurements is in the combustor of a solid fuel ramjet which uses a metallized fuel. High speed motion pictures are being used to study rather large burning particles; (3) In solid propellant rocket motors, metals are used to improve specific impulse and/or to provide damping for combustion pressure oscillations. Particle sizing experiments are being conducted using diode arrays to measure the light intensity as a function of scattering angle; (4) Once a good quality hologram is attained, a need exists for obtaining the particle distributions from hologram in a short period of time. A Quantimet 720 Image Analyzer is being used to reconstruct images.
Powers, John↗
Super Resolving Unrolled Neural Networks for Remote Sensing
In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.
47 OTHER INSTRUMENTATION↗
Rotary-motion-extended Array Synthesis (R-MXAS)
R-MXAS is a revolutionary aerospace architecture for realizing a synthetic aperture imaging radiometer (SAIR) with dramatically lower SWaP than existing state-of-the-art (SOTA) methods. The space-based component of the RMXAS system (Figure 1) is a single platform comprising a 1-D sparse / decimated antenna array on a rigid tether (deployed parallel to the horizon) and one or more additional tethered antennas that rotate in a plane orthogonal to the 1-D array.The processing that correlates the data from these two antenna systems and performs image reconstruction has both space-based and ground-based components. The processing exploits the interferometric baselines formed between the rotating tethered antenna at radius R and each of the antennas of the 1-D array on the rigid tether.
Radiometer↗
Interior soft x-ray tomography with sparse global sampling
To investigate the feasibility of interior imaging reconstruction in soft X-ray tomography for higher-resolution cellular imaging, including whole-cell imaging, we develop an alignment and reconstruction algorithm that combines a small number of sparse whole-cell images with a high-resolution local interior scan. Based on numerical simulations, we demonstrate that combined reconstructions mitigate the depth-of-field limitation in high-resolution scans, enable radiation dose optimization, and yield quantitative X-ray absorption values with sparse sampling. We further validate our numerical approach using experimental data from two different cell types and show that the combined reconstruction reliably provides high spatial resolution within an interior region of interest of a whole cell. The resulting sparse reconstruction framework offers robust, faithful visualization of cellular organelles in soft X-ray tomography. This mesoscale imaging strategy allows one to ‘scout’ and zoom into selected subcellular volumes of interest, enabling increased spatial resolution without sacrificing larger-volume imaging and providing information on the relative positions of all organelles within a cell.
3D imaging↗
A study of a space-station-associated multiple spacecraft Michelson spatial interferometer
One approach to Michelson spatial interferometry at optical wavelengths involves use of an array of spacecraft in which two widely-separated telescopes collect light from a star and direct it to a third, centrally-located, device which combines the beams in order to detect and measure interference fringes. The original version of a spacecraft array for Michelson spatial interferometry (SAMSI) was modified so that the system uses the fuel resupply capability of a space station. The combination of this fuel resupply capability with a method of obtaining image Fourier transform phase information, necessary for full image reconstruction, permits SAMSI to be used to synthesize images equivalent to those produced by huge apertures in space. Synthesis of apertures in the 100 to 500 meter range is discussed. Reconstruction can be performed to a visual magnitude of at least 8 for a 100 A passband in 9 hours. Data are simultaneously collected for image generation from 0.1 micron to 18 microns. In the one-dimensional mode, measurements can be made every 90 minutes (including acquisition and repointing time) for objects as faint as 19th magnitude in the visible.
Stachnik, R. V.↗
Individual and Simultaneous Imaging of ⁹⁹mTc and ¹⁷⁷Lu With a Preclinical Broad Energy-Spectrum CZT-Based SPECT
Radiopharmaceutical therapy has demonstrated a high efficacy in the treatment of various tumor types. One of the radionuclides already used in the clinic is 177Lu, a beta emitter that also emits several photons imageable with SPECT. Quantitative imaging of 177Lu is critical for developing new radiopharmaceuticals. Energy resolution is an important factor when imaging multiple photon emissions. Solid-state detectors offer a superior performance over scintillators, that are commonly used in commercially-available preclinical SPECT scanners. This study demonstrates the feasibility of 99m Tc and 177Lu quantitative imaging in mouse phantoms, individually and simultaneously, with a SPECT prototype built with four CdZnTe (CZT) detector heads and a custom-designed and energy-optimized parallel-hole tungsten collimator. With a custom implementation of the one-step late (OSL) image reconstruction algorithm, the system is capable of imaging energies from ~70 keV to 250 keV. Above 250 keV, images were significantly affected by septal penetration, consistent with the collimator design. A recovery coefficient within 25% was obtained for activities as low as 2 kBq/mL for 99m Tc and 45% for 177Lu. Compared to a commercial NaI-based preclinical SPECT (VECTor4/CT), our prototype showed a superior energy resolution (< 5% at 140 keV), a similar uniformity with a high-compact design.
Encarnação, Pedro M C C↗
Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991
Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.
Worrall, Diana M.↗
Lossless compression of AVIRIS data: Comparison of methods and instrument constraints
A family of lossless compression methods, allowing exact image reconstruction, are evaluated for compressing Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) image data. The methods are used on Differential Pulse Code Modulation (DPCM). The compressed data have an entropy of order 6 bits/pixel. A theoretical model indicates that significantly better lossless compression is unlikely to be achieved because of limits caused by the noise in the AVIRIS channels. AVIRIS data differ from data produced by other visible/near-infrared sensors, such as LANDSAT-TM or SPOT, in several ways. Firstly, the data are recorded at a greater resolution (12 bits, though packed into 16-bit words). Secondly, the spectral channels are relatively narrow and provide continuous coverage of the spectrum so that the data in adjacent channels are generally highly correlated. Thirdly, the noise characteristics of the AVIRIS are defined by the channels' Noise Equivalent Radiances (NER's), and these NER's show that, at some wavelengths, the least significant 5 or 6 bits of data are essentially noise.
Roger, R. E.↗
Enhanced Resolution ERS-1 Scatterometer Imaging of Southern Ocean Sea Ice
A new method of image reconstruction is described which allows enhanced resolution images to be produced from gridded C-band ERS-1 AMI scatterometer data.
enhanced↗
Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images
The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. Published by the American Physical Society 2025
Reed, Niko R. (ORCID:0009000305222403)↗