Charles A. Bouman
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View article: Texture Matching GAN for CT Image Enhancement
Texture Matching GAN for CT Image Enhancement Open
Deep neural networks (DNNs) are commonly used to denoise and sharpen X-ray computed tomography (CT) images with the goal of reducing patient X-ray dosage while maintaining reconstruction quality. However, naive application of DNN-based met…
View article: XCal: model-based approach to X-ray CT spectral calibration
XCal: model-based approach to X-ray CT spectral calibration Open
Transmission X-ray computed tomography (CT) is widely used to quantitatively reconstruct 3D objects composed of multiple materials. However, accurate CT reconstruction requires the system to be calibrated to account for the effective X-ray…
View article: MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction
MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction Open
Residual strain, a tensor quantity, is a critical material property that impacts the overall performance of metal parts. Neutron Bragg edge strain tomography is a technique for imaging residual strain that works by making conventional hype…
View article: Dual-comb mid-infrared spectromicroscopy with photothermal fluorescence detection
Dual-comb mid-infrared spectromicroscopy with photothermal fluorescence detection Open
An approach is described for spectrally parallel hyperspectral mid-infrared imaging with spatial resolution dictated by fluorescence imaging. Quantum cascade laser (QCL)-based dual-comb mid-infrared spectroscopy enables the acquisition of …
View article: An Adaptive View Selection Algorithm for Large-scale Cone-Beam CT Reconstruction
An Adaptive View Selection Algorithm for Large-scale Cone-Beam CT Reconstruction Open
Industrial cone-beam X-ray computed tomography (CT) produces 3D reconstructions of objects using projection measurements taken at multiple predetermined rotation angles around a single axis. Achieving high-quality reconstructions with trad…
View article: hdsullivan/ResSR
hdsullivan/ResSR Open
This is the official implementation of ResSR [1]. ResSR is a computationally efficient MSI-SR method that achieves high-quality reconstructions by using a closed-form spectral decomposition along with a spatial residual correction. ResSR a…
View article: Fast Hyperspectral Reconstruction for Neutron Computed Tomography Using Subspace Extraction
Fast Hyperspectral Reconstruction for Neutron Computed Tomography Using Subspace Extraction Open
Hyperspectral neutron computed tomography enables 3D non-destructive imaging of the spectral characteristics of materials. In traditional hyperspectral reconstruction, the data for each neutron wavelength bin is reconstructed separately. T…
View article: Fast Hyperspectral Neutron Tomography
Fast Hyperspectral Neutron Tomography Open
Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each…
View article: Data-driven synthetic wavefront generation for boundary layer data
Data-driven synthetic wavefront generation for boundary layer data Open
Disturbances such as atmospheric turbulence and aero-optic effects lead to\nwavefront aberrations, which degrade performance in imaging and laser\npropagation applications. Adaptive optics (AO) provide a method to mitigate\nthese effects b…
View article: Super Resolving Unrolled Neural Networks for Remote Sensing
Super Resolving Unrolled Neural Networks for Remote Sensing Open
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 inclu…
View article: ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images Open
Multispectral imaging sensors typically have wavelength-dependent resolution, which limits downstream processing. Consequently, researchers have proposed multispectral image super-resolution (MSI-SR) methods which upsample low-resolution b…
View article: Edge Projection-Based Adaptive View Selection for Cone-Beam CT
Edge Projection-Based Adaptive View Selection for Cone-Beam CT Open
Industrial cone-beam X-ray computed tomography (CT) scans of additively manufactured components produce a 3D reconstruction from projection measurements acquired at multiple predetermined rotation angles of the component about a single axi…
View article: A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems
A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems Open
View article: Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects
Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects Open
Flash X-ray computed tomography (CT) is an important imaging modality for characterization of high-speed dynamic events, such as Kolsky bar impact experiments for the study of mechanical properties of materials subjected to impulsive force…
View article: Pixel-weighted Multi-pose Fusion for Metal Artifact Reduction in X-ray Computed Tomography
Pixel-weighted Multi-pose Fusion for Metal Artifact Reduction in X-ray Computed Tomography Open
X-ray computed tomography (CT) reconstructs the internal morphology of a three dimensional object from a collection of projection images, most commonly using a single rotation axis. However, for objects containing dense materials like meta…
View article: CLAMP: Majorized Plug-and-Play for Coherent 3D LIDAR Imaging
CLAMP: Majorized Plug-and-Play for Coherent 3D LIDAR Imaging Open
Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these metho…
View article: Dual-Comb Mid-Infrared Spectromicroscopy with Photothermal Fluorescence Detection
Dual-Comb Mid-Infrared Spectromicroscopy with Photothermal Fluorescence Detection Open
An approach is described for spectrally parallel hyperspectral mid-infrared imaging with spatial resolution dictated by fluorescence imaging. Quantum cascade laser (QCL)-based dual-comb mid-infrared spectroscopy enables acquisition of infr…
View article: Evaluating and improving local hyperspectral anomaly detectors
Evaluating and improving local hyperspectral anomaly detectors Open
View article: Weak signal detection in hyperspectral imagery using sparse matrix transform (SMT) covariance estimation
Weak signal detection in hyperspectral imagery using sparse matrix transform (SMT) covariance estimation Open
Many detection algorithms in hyperspectral image analysis, from well-characterized gaseous and solid targets to deliberately uncharacterized anomalies and anomlous changes, depend on accurately estimating the covariance matrix of the backg…
View article: MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data
MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data Open
X-ray computed tomography (CT) based on photon counting detectors (PCD) extends standard CT by counting detected photons in multiple energy bins. PCD data can be used to increase the contrast-to-noise ratio (CNR), increase spatial resoluti…
View article: TRINIDI: Time-of-Flight Resonance Imaging With Neutrons for Isotopic Density Inference
TRINIDI: Time-of-Flight Resonance Imaging With Neutrons for Isotopic Density Inference Open
Accurate reconstruction of 2D and 3D isotope densities is a desired capability with great potential impact in applications such as evaluation and development of next-generation nuclear fuels. Neutron time-of-flight (TOF) resonance imaging …
View article: Texture Matching GAN for CT Image Enhancement
Texture Matching GAN for CT Image Enhancement Open
Deep neural networks (DNN) are commonly used to denoise and sharpen X-ray computed tomography (CT) images with the goal of reducing patient X-ray dosage while maintaining reconstruction quality. However, naive application of DNN-based meth…
View article: Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography
Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography Open
Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to health concerns. But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction. As a result, low signal cor…
View article: MBIR Training for a 2.5D DL network in X-ray CT
MBIR Training for a 2.5D DL network in X-ray CT Open
In computed tomographic imaging, model based iterative reconstruction methods have generally shown better image quality than the more traditional, faster filtered backprojection technique. The cost we have to pay is that MBIR is computatio…
View article: Design of Novel Loss Functions for Deep Learning in X-ray CT
Design of Novel Loss Functions for Deep Learning in X-ray CT Open
Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of data rather than explicitly designed allows them to learn sign…
View article: The Foundations of Computational Imaging: A signal processing perspective
The Foundations of Computational Imaging: A signal processing perspective Open
Twenty-five years ago, the field of computational imaging arguably did not exist, at least not as a standalone arena of research activity and technical development. Of course, the idea of using computation to form images had been around fo…
View article: Table of Contents
Table of Contents Open
View article: Generative Plug and Play: Posterior Sampling for Inverse Problems
Generative Plug and Play: Posterior Sampling for Inverse Problems Open
Over the past decade, Plug-and-Play (PnP) has become a popular method for reconstructing images using a modular framework consisting of a forward and prior model. The great strength of PnP is that an image denoiser can be used as a prior m…
View article: An Edge Alignment-Based Orientation Selection Method for Neutron Tomography
An Edge Alignment-Based Orientation Selection Method for Neutron Tomography Open
Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of…
View article: Dynamic DH-MBIR for Phase-Error Estimation from Streaming Digital-Holography Data
Dynamic DH-MBIR for Phase-Error Estimation from Streaming Digital-Holography Data Open
Directed energy applications require the estimation of digital-holographic (DH) phase errors due to atmospheric turbulence in order to accurately focus the outgoing beam. These phase error estimates must be computed with very low latency t…