no code implementations • 11 Feb 2024 • Evan Bell, Michael T. McCann, Marc Klasky
In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system.
no code implementations • 25 May 2023 • Michael T. McCann, Hyungjin Chung, Jong Chul Ye, Marc L. Klasky
This paper explores the use of score-based diffusion models for Bayesian image reconstruction.
no code implementations • 10 Mar 2023 • Michael T. McCann, Elena Guardincerri, Samuel M. Gonzales, Lauren A. Misurek, Jennifer L. Schei, Marc L. Klasky
We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware.
1 code implementation • CVPR 2023 • Hyungjin Chung, Dohoon Ryu, Michael T. McCann, Marc L. Klasky, Jong Chul Ye
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.
2 code implementations • 29 Sep 2022 • Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky, Jong Chul Ye
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 solvers.
no code implementations • 18 Jul 2022 • Avrajit Ghosh, Michael T. McCann, Madeline Mitchell, Saiprasad Ravishankar
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images.
no code implementations • 2 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.
no code implementations • 21 Nov 2021 • 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.
no code implementations • 15 Oct 2021 • Alexander N. Sietsema, Michael T. McCann, Marc L. Klasky, Saiprasad Ravishankar
In this paper, we compare idealized versions of these two approaches with synthetic experiments.
no code implementations • 26 Mar 2021 • Zhishen Huang, Siqi Ye, Michael T. McCann, Saiprasad Ravishankar
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
no code implementations • 11 Dec 2020 • Michael T. McCann, Marc L. Klasky, Jennifer L. Schei, Saiprasad Ravishankar
To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it.
no code implementations • 6 Oct 2020 • Siqi Ye, Zhipeng Li, Michael T. McCann, Yong Long, Saiprasad Ravishankar
The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.
no code implementations • 9 Jun 2020 • Michael T. McCann, Saiprasad Ravishankar
We present a method for supervised learning of sparsity-promoting regularizers for image denoising.
1 code implementation • 19 Dec 2018 • Emmanuel Soubies, Ferréol Soulez, Michael T. McCann, Thanh-an Pham, Laurène Donati, Thomas Debarre, Daniel Sage, Michael Unser
GlobalBioIm is an open-source MATLAB library for solving inverse problems.
Mathematical Software
no code implementations • 12 Jun 2018 • Michael T. McCann, Vincent Andrearczyk, Michael Unser, Adrien Depeursinge
In this work, we propose an algorithm for a rotational version of sparse coding that is based on K-SVD with additional rotation operations.
2 code implementations • 11 Oct 2017 • 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.
no code implementations • 6 Sep 2017 • Harshit Gupta, Kyong Hwan Jin, Ha Q. Nguyen, Michael T. McCann, Michael Unser
When the projector is replaced with a CNN, we propose a relaxed PGD, which always converges.
no code implementations • 11 Nov 2016 • Kyong Hwan Jin, Michael T. McCann, Emmanuel Froustey, Michael Unser
The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution.
no code implementations • 8 Jun 2016 • 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 coefficients.