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Found 28 papers, 1 papers with code
X-ray Spectral Estimation using Dictionary Learning
As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities.
Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography
The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain.
TRINIDI: Time-of-Flight Resonance Imaging with Neutrons for Isotopic Density Inferenc
We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel.
Ringing Artifact Reduction Method for Ultrasound Reconstruction Using Multi-Agent Consensus Equilibrium
Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data.
An Edge Alignment-based Orientation Selection Method for Neutron Tomography
Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences.
Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium
In this paper, we present Multi-Pose Fusion, a novel algorithm that performs a joint tomographic reconstruction from CT scans acquired from multiple poses of a single object, where each pose has a distinct rotation axis.
Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.
Sparse-View CT Reconstruction using Recurrent Stacked Back Projection
Sparse-view CT reconstruction is important in a wide range of applications due to limitations on cost, acquisition time, or dosage.
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics.
Projected Multi-Agent Consensus Equilibrium for Ptychographic Image Reconstruction
Ptychography is a computational imaging technique using multiple, overlapping, coherently illuminated snapshots to achieve nanometer resolution by solving a nonlinear phase-field recovery problem.
CodEx: A Modular Framework for Joint Temporal De-blurring and Tomographic Reconstruction
The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm.
Hyperspectral Neutron CT with Material Decomposition
Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material.
Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches
In the first part, we will focus on model-based image reconstruction algorithms that formulate the inversion as solving a high-dimensional optimization problem involving a data-fidelity term and a regularization term.
Ultra-Sparse View Reconstruction for Flash X-Ray Imaging using Consensus Equilibrium
A growing number of applications require the reconstructionof 3D objects from a very small number of views.
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction
In this paper, we present multi-slice fusion, a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers.
Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography
Our algorithm uses precise physics models to reconstruct from the native cone-beam geometry and interleaved dual source helical trajectory of a DS-FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture.
4D X-Ray CT Reconstruction using Multi-Slice Fusion
In this paper, we present Multi-Slice Fusion, a novel algorithm for 4D and higher-dimensional reconstruction, based on the fusion of multiple low-dimensional denoisers.
Plug and play methods for magnetic resonance imaging (long version)
In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery.
Deep Back Projection for Sparse-View CT Reconstruction
Filtered back projection (FBP) is a classical method for image reconstruction from sinogram CT data.
SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks
From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar.
Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling
Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage.
Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework
The estimation of phase errors from digital-holography data is critical for applications such as imaging or wave-front sensing.
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium
We give examples to illustrate consensus equilibrium and the convergence properties of these algorithms and demonstrate this method on some toy problems and on a denoising example in which we use an array of convolutional neural network denoisers, none of which is tuned to match the noise level in a noisy image but which in consensus can achieve a better result than any of them individually.
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio.
A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)
In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements.
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy
Consequently, there is an enormous demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution.
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT
In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction.
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation.
