7 papers
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…
Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction
Tao Hong, Zhaoyi Xu, Jason Hu +1
Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, nonsmooth, and sometimes…
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…
CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
Bowen Song, Zecheng Zhang, Zhaoxu Luo +6
Diffusion models have emerged as powerful tools for generative tasks, producing high-quality outputs across diverse domains. However, how the generated data responds to the initial…
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…
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction
Tao Hong, Xiaojian Xu, Jason Hu +1
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of…