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Found 16 papers, 0 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.
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.
Model-based Reconstruction for Multi-Frequency Collimated Beam Ultrasound Systems
Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells.
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.
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.
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.
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics.
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.
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.
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.
