6 papers
Generative Translation Priors: Bayesian Imaging with Cross-Modality Image Translation
Evan Bell, Jiaming Liu, Yifan Chen +1
The ability to leverage images from co-available modalities to inform target-domain reconstruction is highly desirable in imaging algorithms. In this work, we introduce Generative…
Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors
Deliang Wei, Evan Bell, Wenhan Guo +2
Bayesian imaging inverse problems often require sampling from high-dimensional posterior distributions. While recent score-based and diffusion models provide expressive Bayesian pr…
Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
Evan Bell, Shijun Liang, Ismail Alkhouri +1
Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstr…
Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory
Ismail Alkhouri, Evan Bell, Avrajit Ghosh +3
In recent years, deep learning methods have been extensively developed for inverse imaging problems (IIPs), encompassing supervised, self-supervised, and generative approaches. Mos…
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
Shijun Liang, Evan Bell, Avrajit Ghosh +1
Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimen…
Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction
Shijun Liang, Evan Bell, Qing Qu +2
The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medi…