Results for “Image reconstruction” — Page 19

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRS1994

Long-Slit Spectra of Mars in the Thermal Infrared

We have obtained long-slit spectra of Mars in the 7-14 micrometer regime. Our two integrations, taken 1993 March 2, are spaced about 2 hours apart and are centered at longitudes of roughly 210 and 240 degrees. The data cover the Elysium Planitia and volcanic regions, the classical dark albedo regions of Cerburus and central and eastern Hesperia, and the classical bright albedo region Utopia Planitia. We have deconvolved the point spread function from the images using a maximum entropy reconstruction algorithm; the resulting spatial resolution of the images is better than 1". Mars occupies approximately 9" of the slit, and we have detected variations in the spectral emission from different areas of the planet. We are in the process of interpreting these spectral variations in terms of the temperature and composition of the surface and the transmission of the Martian atmospheric dust and gases.

Rowland, C. M.↗

NASA NTRS1992

Efficient transfer of images over networks

Effective remote observing requires sending large images over long distances. The usual approach to the transfer problem is to require high bandwidth transmission links, which are expensive to install and operate. An alternative approach is to use existing low-bandwidth connections, such as phone lines or the Internet, in a highly efficient manner by compressing the images. The combined use of existing low-cost infrastructure and standard networking software means that remote observing can be made practical even for small observatories with limited network resources. The authors have implemented such a scheme based on the H-transform compression method developed for astronomical images, which are often resistant to compression because they are noisy. The H-transform can be used for either lossy or lossless compression, and compression factors of at least 10 can be achieved with no noticeable losses in the astrometric or photometric properties of the compressed images. The H-transform allows us to organize the information in an image so that the 'useful' information can be sent first, followed by the noise, which makes up the bulk of the transmission. The receiver can invert a partially received set of H-coefficients, creating an image that improves with time. The H-transform is particularly well-suited to this style of incremental reconstruction, because the spatially localized nature of the basis functions of the H-transorm prevents the appearance of artifacts such as ringing around point sources and edges. The authors' implementation uses the WIYN Telescope Control System's TCP-based communications protocol. An 800x800 16-bit astronomical image was sent over a 2400 baud connection, which would normally take about 71 minutes; after only 60 seconds, the partially received H-transform produced an image that did not differ appreciably from the original. This poster presents a quantification of the efficiencies, as well as examples of images reconstructed from partial data.

Percival, J. W.↗

NASA NTRS1984

Automatic holographic droplet analysis for liquid fuel sprays

The basic scheme for automated holographic analysis involves an optical system for reconstruction of the three dimensional real image of the droplet field, a spatial scanning system to transport a digitizing X-y image sensor through the real image, and processing algorithms for droplet recognition which establish the droplet sizes and positions. The hardware for system demonstrated includes the expanded and collimated beam from a 5 mW helium-neon laser for hologram reconstruction, an imaging lens for magnification of the real image field, and a video camera and digitizer providing 512-by-512 pixel resolution with 8-bit digitization. A mechanical stage is used to scan the hologram in three dimensional space, maintaining constant image magnification. A test droplet hologram is used for development and testing of the image processing algorithms.

Stanton, A. C.↗

NASA NTRS2022

VLTI-MATISSE L- and N-Band Aperture-Synthesis Imaging of the Unclassified B[e] star FS Canis Majoris

Context. FS Canis Majoris (FS CMa, HD 45677) is an unclassified B[e] star surrounded by an inclined dust disk. The evolutionary stage of FS CMa is still debated. Perpendicular to the circumstellar disk, a bipolar outflow was detected. Infrared aperture-synthesis imaging provides us with a unique opportunity to study the disk structure. Aims. Our aim is to study the intensity distribution of the disk of FS CMa in the mid-infrared L and N bands. Methods. We performed aperture-synthesis imaging of FS CMa with the MATISSE instrument (Multi AperTure mid-Infrared SpectroScopic Experiment) in the low spectral resolution mode to obtain images in the L and N bands. We computed radiative transfer models that reproduce the L- and N-band intensity distributions of the resolved disks. Results. We present L- and N-band aperture-synthesis images of FS CMa reconstructed in the wavelength bands of 3.4–3.8 and 8.6–9.0 μm. In the L-band image, the inner rim region of an inclined circumstellar disk and the central object can be seen with a spatial resolution of 2.7 milliarcsec (mas). An inner disk cavity with an angular diameter of ~6 × 12 mas is resolved. The L-band disk consists of a bright northwestern (NW) disk region and a much fainter southeastern (SE) region. The images suggest that we are looking at the bright inner wall of the NW disk rim, which is on the far side of the disk. In the N band, only the bright NW disk region is seen. In addition to deriving the inclination and the inner disk radius, fitting the reconstructed brightness distributions via radiative transfer modelling allows one to constrain the innermost disk structure, in particular the shape of the inner disk rim.

techniques↗

NASA NTRS2008

Parallel Computing for the Computed-Tomography Imaging Spectrometer

This software computes the tomographic reconstruction of spatial-spectral data from raw detector images of the Computed-Tomography Imaging Spectrometer (CTIS), which enables transient-level, multi-spectral imaging by capturing spatial and spectral information in a single snapshot.

Lee, Seungwon↗

NASA NTRS1985

A 94 GHz imaging array using slot line radiators

A planar endfire slotted-line antenna structure was investigated. It was found that the H-plane beamwidths are basically dependent upon the substrate properties, whereas the E-plane beamwidths are more strongly a function of the slot's shape and size. It is shown that these antennas produce symmetrical E and H-plane beamwidths while following Zucker's standard traveling-wave antenna beamwidth curves over some range of antenna normalized length. An empircally derived design formula for effective substrate thickness is shown to predict this range for linearly tapered slotted-line antennas. The experimental imaging properties of these arrays are presented and imaging theory is discussed. It is shown that a minimum spacing of elements is necessary for exact reconstruction for a sampled image in a diffraction limited system. Because these LTSA elements employ the traveling-wave mechanism of radiation, they can be spaced two times closer than a conical feed horn of comparable beamwidth.

Korzeniowski, T. L.↗

NASA NTRS1972

Image correlation and sampling study

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed.

Popp, D. J.↗

NASA NTRS2009

Trajectory Reconstruction of the ST-9 Sounding Rocket Experiment Using IMU and Landmark Data

This paper presents trajectory reconstruction of the ST-9 sounding rocket experiment using the onboard IMU data and descent imagery. The raw IMU accelerometer measurements are first converted into inertial acceleration and then used in trajectory integration. The descent images are pre-processed using a map-matching algorithm and unique landmarks for each image are created. Using the converted IMU data and descent images, the result from dead-reckoning and the kinematic-fix approaches are first compared with the GPS measurements. Then, both the IMU data and landmarks are processed together using a batch least-squares filter and the position, velocity, stochastic acceleration, and camera orientation of each image are estimated. The reconstructed trajectory is compared with the GPS data and the corresponding formal uncertainties are presented. The result shows that IMU data and descent images processed with a batch filter algorithm provide the trajectory accuracy required for pin-point landing.

Park, Ryan S.↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS1989

An image compression algorithm for a high-resolution digital still camera

The Electronic Still Camera (ESC) project will provide for the capture and transmission of high-quality images without the use of film. The image quality will be superior to video and will approach the quality of 35mm film. The camera, which will have the same general shape and handling as a 35mm camera, will be able to send images to earth in near real-time. Images will be stored in computer memory (RAM) in removable cartridges readable by a computer. To save storage space, the image will be compressed and reconstructed at the time of viewing. Both lossless and loss-y image compression algorithms are studied, described, and compared.

Nerheim, Rosalee↗

NASA NTRS1991

Multiple image SAR shape-from-shading

A technique for combining radar image shape-from-shading with stereo radargrammetry to produce terrain surface models using multiple SAR images is described. This technique is expected to be of use to reconstruct surface shape from Magellan images of planet Venus, and to refine the results of terrestrial radar measurements. Local variation in pixel shading is an indicator of terrain slope changes. This variation in pixel shading offers an opportunity for increasing the detail of terrain mapping over that which is available from stereo radargrammetry alone. Shape-from-shading can potentially provide a relative change in height at each pixel. This leads to a dense set of height measurements and a more faithful rendition of the local terrain shapes. However, shape-from-shading needs some type of boundary values or external terrain low-frequency information to succeed. These can be obtained from stereo or from altimeter measurements.

Thomas, J.↗

NASA NTRS1985

On stereo viewing of SAR images

Stereo viewing is indispensable for the measurement of homologue points in two overlapping images and the subsequent reconstruction of the three-dimensional topographic relief. Aspects of stereo viewing geometry are discussed along with problems of radar stereoscopy, taking into account stereo evaluation with an exaggeration factor, the viewability of real images, and radar stereoscopic computations. Attention is given to the definition of radar stereoscopic exaggeration, questions of image simulation, and the accuracy obtained with parallax measurements in radar images. An evaluation with a large set of about 40 radar stereo models demonstrates that same-side arrangements provide good stereo viewability. This result was confirmed for aircraft radar with look angles off-nadir of 60-80 deg and intersection angles between 0.2 deg and 23 deg, and for satellite radar (Seasat) with look angles of 20 deg and interaction angles of 1.2 deg - 4.8 deg.

Leberl, F. W.↗

NASA NTRS2021

VLTI-MATISSE Chromatic Aperture-Synthesis Imaging of η Carinae’s Stellar Wind Across the Brα Line: Periastron Passage Observations in February 2020

Context: Eta Carinae is a highly eccentric, massive binary system (semimajor axis∼15.5 au) with powerful stellar winds and a phase-dependent wind-wind collision (WWC) zone. The primary star, ηCar A, is a luminous blue variable (LBV); the secondary, η Car B, is a Wolf-Rayet or O star with a faster but less dense wind. Aperture-synthesis imaging allows us to study the mass loss from the enigmatic LBV η Car. Understanding LBVs is a crucial step toward improving our knowledge about massive stars and their evolution. Aims: Our aim is to study the intensity distribution and kinematics of η Car’s WWC zone. Methods: Using the VLTI-MATISSE mid-infrared interferometry instrument, we perform Brα imaging of η Car’s distorted wind. Results: We present the first VLTI-MATISSE aperture-synthesis images of η Car A’s stellar wind in several spectral channels distributed across the Brα 4.052 μm line (spectral resolving power R∼960). Our observations were performed close to periastron passage in February 2020 (orbital phase∼14.0022). The reconstructed iso-velocity images show the dependence of the primary stellar wind on wavelength or line-of-sight (LOS) velocity with a spatial resolution of 6 mas (∼14 au). The radius of the faintest outer wind regions is∼26 mas (∼60 au). At several negative LOS velocities, the primary stellar wind is less extended to the northwest than in other directions. This asymmetry is most likely caused by the WWC. Therefore, we see both the velocity field of the undisturbed primary wind and the WWC cavity. In continuum spectral channels, the primary star wind is more compact than in line channels. A fit of the observed continuum visibilities with the visibilities of a stellar wind CMFGEN model (CMFGEN is an atmosphere code developed to model the spectra of a variety of objects) provides a full width at half maximum fit diameter of the primary stellar wind of 2.84±0.06 mas (6.54±0.14 au). We compare the derived intensity distributions with the CMFGEN stellar wind model and hydrodynamic WWC models.

G Weigelt↗

NASA NTRS1994

High-Resolution Gamma-Ray Imaging Measurements Using Externally Segmented Germanium Detectors

Fully two-dimensional gamma-ray imaging with simultaneous high-resolution spectroscopy has been demonstrated using an externally segmented germanium sensor. The system employs a single high-purity coaxial detector with its outer electrode segmented into 5 distinct charge collection regions and a lead coded aperture with a uniformly redundant array (URA) pattern. A series of one-dimensional responses was collected around 511 keV while the system was rotated in steps through 180 degrees. A non-negative, linear least-squares algorithm was then employed to reconstruct a 2-dimensional image. Corrections for multiple scattering in the detector, and the finite distance of source and detector are made in the reconstruction process.

spectroscopy gamma ray germanium sensor nucleosynt↗

NASA NTRS1994

Microwave imaging of metal objects

The procedure of microwave imaging by maximum entropy method is discussed. First, the relationship between the induced current on the metal object surface and the scattered field is introduced. Our imaging concept is to reconstruct the induced current on the object surface from the measured scattered field. The object configuration will be provided by the induced current which is zero everywhere except on the object surface. Future work is also included with focus on the application of microwave imaging to both NASA and industry.

Krishen, Kumar↗

NASA NTRSDate not supplied

Using Multiplicative Algebraic Reconstruction Techniques (MART) to Derive Instrument Requirements for Computed Tomography Imaging Spectrographs

Computed Tomography Imaging Spectrographs (CTISs), which are generally slitless or large-aperture spectrographs that observe a wide, dispersed field of view in multiple dispersion angles or diffraction orders, provide a unique opportunity to capture spectral information over an extended source, such as the Sun, but require software reconstruction techniques to be fully utilized. A useful data product that can be recovered from CTIS observations is intensity maps of solar features in single spectral lines. These intensity maps can then be used to determine temperature, density, abundance, and equilibrium properties of the emitting plasma. We apply the multiplicative algebraic reconstruction technique (MART) to example data from a variety of CTIS configurations to determine the capability and limitations of the method to return spectrally-pure maps. By completing this study, we aim to establish a path to derive requirements for CTIS instruments that rely on reconstruction techniques to meet their science objectives.

Amy Winebarger↗