9 citations · 9 across the 1 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…