Search Results for author:
Found 19 papers, 4 papers with code
Supervised Reconstruction for Silhouette Tomography
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.
Score-based Diffusion Models for Bayesian Image Reconstruction
This paper explores the use of score-based diffusion models for Bayesian image reconstruction.
Material Identification From Radiographs Without Energy Resolution
We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware.
Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
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.
Diffusion Posterior Sampling for General Noisy Inverse Problems
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.
Learning Sparsity-Promoting Regularizers using Bilevel Optimization
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images.
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics.
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)
We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems.
Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT
In this paper, we compare idealized versions of these two approaches with synthetic experiments.
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
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.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
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.
Supervised Learning of Sparsity-Promoting Regularizers for Denoising
We present a method for supervised learning of sparsity-promoting regularizers for image denoising.
Pocket Guide to Solve Inverse Problems with GlobalBioIm
GlobalBioIm is an open-source MATLAB library for solving inverse problems.
Mathematical Software
Fast Rotational Sparse Coding
In this work, we propose an algorithm for a rotational version of sparse coding that is based on K-SVD with additional rotation operations.
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging.
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
When the projector is replaced with a CNN, we propose a relaxed PGD, which always converges.
Deep Convolutional Neural Network for Inverse Problems in Imaging
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.
Rotation Invariant Angular Descriptor Via A Bandlimited Gaussian-like Kernel
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.
