14 citations · 20 across the 9 of their papers we have counts for
4 papers · 1 filter
Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction
Taewon Yang, Jason Hu, Jeffrey A. Fessler +1
Diffusion models learn strong image priors that can be leveraged to solve inverse problems like medical image reconstruction. However, for real-world applications such as 3D Comput…
Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problems
Jason Hu, Bowen Song, Jeffrey A. Fessler +1
Diffusion models have achieved excellent success in solving inverse problems due to their ability to learn strong image priors, but existing approaches require a large training dat…
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
Bowen Song, Jason Hu, Zhaoxu Luo +2
Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT). Due to the demanding m…
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
Jason Hu, Bowen Song, Xiaojian Xu +2
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and r…