2.6k citations · 4.1k across the 18 of their papers we have counts for
19 papers
Score-based Diffusion Models for Bayesian Image Reconstruction
Michael T. McCann, Hyungjin Chung, Jong Chul Ye +1
This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can…
Material Identification From Radiographs Without Energy Resolution
Michael T. McCann, Elena Guardincerri, Samuel M. Gonzales +3
We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, includ…
Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
Hyungjin Chung, Dohoon Ryu, Michael T. McCann +2
Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility. They ha…
Diffusion Posterior Sampling for General Noisy Inverse Problems
Hyungjin Chung, Jeongsol Kim, Michael T. Mccann +2
Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative…
Learning Sparsity-Promoting Regularizers using Bilevel Optimization
Avrajit Ghosh, Michael T. McCann, Madeline Mitchell +1
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images. Sparsity-promoting regularization is a key ingredient in solving mo…
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Maliha Hossain, Balasubramanya T. Nadiga, Oleg Korobkin +7
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics. This technique has been used in num…
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)
Avrajit Ghosh, Michael T. Mccann, Saiprasad Ravishankar
We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems. The parameters of the regularizer ar…
Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT
Alexander N. Sietsema, Michael T. McCann, Marc L. Klasky +1
In this work, we compare one-step and two-step approaches for X-ray computed tomography (CT) scatter correction and density reconstruction. X-ray CT is an important imaging techniq…
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Zhishen Huang, Siqi Ye, Michael T. McCann +1
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements. Model-base…
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
Michael T. McCann, Marc L. Klasky, Jennifer L. Schei +1
We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a train…
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Siqi Ye, Zhipeng Li, Michael T. McCann +2
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typ…
Supervised Learning of Sparsity-Promoting Regularizers for Denoising
Michael T. McCann, Saiprasad Ravishankar
We present a method for supervised learning of sparsity-promoting regularizers for image denoising. Sparsity-promoting regularization is a key ingredient in solving modern image re…
Pocket Guide to Solve Inverse Problems with GlobalBioIm
Emmanuel Soubies, Ferréol Soulez, Michael T. McCann +5
GlobalBioIm is an open-source MATLAB library for solving inverse problems. The library capitalizes on the strong commonalities between forward models to standardize the resolution…
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks
Michael T. McCann, Michael Unser
This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches.…
Fast Rotational Sparse Coding
Michael T. McCann, Vincent Andrearczyk, Michael Unser +1
We propose an algorithm for rotational sparse coding along with an efficient implementation using steerability. Sparse coding (also called dictionary learning) is an important tech…
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
Michael T. McCann, Kyong Hwan Jin, Michael Unser
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large…
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
Harshit Gupta, Kyong Hwan Jin, Ha Q. Nguyen +2
We present a new method for image reconstruction which replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). CNNs trained as high-…
Deep Convolutional Neural Network for Inverse Problems in Imaging
Kyong Hwan Jin, Michael T. McCann, Emmanuel Froustey +1
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as t…
Rotation Invariant Angular Descriptor Via A Bandlimited Gaussian-like Kernel
Michael T. McCann, Matthew Fickus, Jelena Kovacevic
We present a new smooth, Gaussian-like kernel that allows the kernel density estimate for an angular distribution to be exactly represented by a finite number of its Fourier series…