6 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…
A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers
Tao Hong, Umberto Villa, Jeffrey A. Fessler
Model-based reconstruction plays a key role in compressed sensing (CS) MRI, as it incorporates effective image regularizers to improve the quality of reconstruction. The Plug-and-P…
ALPCAHUS: Subspace Clustering for Heteroscedastic Data
Javier Salazar Cavazos, Jeffrey A Fessler, Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend PCA to the union of subspace (UoS) sett…
Smooth optimization using global and local low-rank regularizers
Rodrigo A. Lobos, Javier Salazar Cavazos, Raj Rao Nadakuditi +1
Many inverse problems and signal processing problems involve low-rank regularizers based on the nuclear norm. Commonly, proximal gradient methods (PGM) are adopted to solve this ty…
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Siyi Chen, Yixuan Jia, Qing Qu +2
Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman, particle) presuppose full know…
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…