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Found 9 papers, 3 papers with code
Score-based Diffusion Models for Bayesian Image Reconstruction
This paper explores the use of score-based diffusion models for Bayesian image reconstruction.
RED-PSM: Regularization by Denoising of Partially Separable Models for Dynamic Imaging
In particular, in the case of dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed.
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.
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time.
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.
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.
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.
