158 citations · 366 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
Berk Iskender, Marc L. Klasky, Yoram Bresler
In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly…
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Anish Lahiri, Marc Klasky, Jeffrey A. Fessler +1
Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the num…