Search Results for author: Charles A. Bouman

Found 22 papers, 1 papers with code

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging

no code implementations31 Mar 2022 Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard, Brendt Wohlberg

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

no code implementations9 Dec 2021 Wenrui Li, Gregery T. Buzzard, Charles A. Bouman

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

no code implementations2 Dec 2021 Maliha Hossain, Balasubramanya T. Nadiga, Oleg Korobkin, Marc L. Klasky, Jennifer L. Schei, Joshua W. Burby, Michael T. McCann, Trevor Wilcox, Soumi De, Charles A. Bouman

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

no code implementations28 Nov 2021 Qiuchen Zhai, Brendt Wohlberg, Gregery T. Buzzard, Charles A. Bouman

Ptychography is a computational imaging technique using multiple, overlapping, coherently illuminated snapshots to achieve nanometer resolution by solving a nonlinear phase-field recovery problem.

Image Reconstruction

Hyperspectral Neutron CT with Material Decomposition

no code implementations6 Oct 2021 Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman

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

no code implementations16 Apr 2021 S. V. Venkatakrishnan, K. Aditya Mohan, Amir Koushyar Ziabari, Charles A. Bouman

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.

Computed Tomography (CT) Image Reconstruction

Ultra-Sparse View Reconstruction for Flash X-Ray Imaging using Consensus Equilibrium

no code implementations29 Mar 2021 Maliha Hossain, Shane C. Paulson, Hangjie Liao, Weinong W. Chen, Charles A. Bouman

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

no code implementations1 Aug 2020 Soumendu Majee, Thilo Balke, Craig A. J. Kemp, Gregery T. Buzzard, Charles A. Bouman

In this paper, we present multi-slice fusion, a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers.

Denoising

Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography

no code implementations26 Jan 2020 Xiao Wang, Robert D. MacDougall, Peng Chen, Charles A. Bouman, Simon K. Warfield

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.

Computed Tomography (CT)

4D X-Ray CT Reconstruction using Multi-Slice Fusion

no code implementations15 Jun 2019 Soumendu Majee, Thilo Balke, Craig A. J. Kemp, Gregery T. Buzzard, Charles A. Bouman

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.

Denoising Low-Dose X-Ray Ct Reconstruction

Deep Back Projection for Sparse-View CT Reconstruction

no code implementations6 Jul 2018 Dong Hye Ye, Gregery T. Buzzard, Max Ruby, Charles A. Bouman

Filtered back projection (FBP) is a classical method for image reconstruction from sinogram CT data.

Image Reconstruction

SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks

1 code implementation8 Mar 2018 Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak

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

no code implementations27 Jun 2017 Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak

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

no code implementations9 Jun 2017 Casey J. Pellizzari, Mark F. Spencer, Charles A. Bouman

The estimation of phase errors from digital-holography data is critical for applications such as imaging or wave-front sensing.

Image Reconstruction

Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium

no code implementations24 May 2017 Gregery T. Buzzard, Stanley H. Chan, Suhas Sreehari, Charles A. Bouman

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.

Denoising Image Reconstruction

Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

no code implementations CVPR 2017 Yandong Guo, Cheng Lu, Jan P. Allebach, Charles A. Bouman

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.

Dictionary Learning Image Compression

A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)

no code implementations14 Mar 2017 G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Michael D. Uchic, Michael A. Groeber, Gregery T. Buzzard, Charles A. Bouman

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

no code implementations28 Nov 2016 Suhas Sreehari, S. V. Venkatakrishnan, Katherine L. Bouman, Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman

Consequently, there is an enormous demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution.

Super-Resolution

A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT

no code implementations12 May 2016 Ruoqiao Zhang, Dong Hye Ye, Debashish Pal, Jean-Baptiste Thibault, Ken D. Sauer, Charles A. Bouman

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.

Image Denoising

Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

no code implementations23 Dec 2015 Suhas Sreehari, S. V. Venkatakrishnan, Brendt Wohlberg, Lawrence F. Drummy, Jeffrey P. Simmons, Charles A. Bouman

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.

Denoising Electron Tomography

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