1 citations · 1 across the 8 of their papers we have counts for
11 papers
SNaP: One-Step Posterior Sampling for Noisy Inverse Problems
Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1
Diffusion and flow-matching models can produce high-quality posterior samples for inverse problems, but typically require tens to thousands of network evaluations per draw. MeanFlo…
FIRM: Flow-based Imaging via Regularized Minimization
Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1
Flow matching methods for imaging inverse problems typically incorporate measurements through network conditioning or guidance during sampling. Neither approach explicitly applies…
NullFlow: One-Step Generative Reconstruction
Xiao Shi, Edward P. Chandler, Chicago Y. Park +2
We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measurement-consistent subspace. Becau…
Stochastic Generative Plug-and-Play Priors
Chicago Y. Park, Edward P. Chandler, Yuyang Hu +4
Plug-and-play (PnP) methods are widely used for solving imaging inverse problems by incorporating a denoiser into optimization algorithms. Score-based diffusion models (SBDMs) have…
Moments Matter: Posterior Recovery in Poisson Denoising via Log-Networks
Shirin Shoushtari, Edward P. Chandler, Ulugbek S. Kamilov
Poisson denoising plays a central role in photon-limited imaging applications such as microscopy, astronomy, and medical imaging. It is common to train deep learning models for den…
Analysis Plug-and-Play Methods for Imaging Inverse Problems
Edward P. Chandler, Shirin Shoushtari, Brendt Wohlberg +1
Plug-and-Play Priors (PnP) is a popular framework for solving imaging inverse problems by integrating learned priors in the form of denoisers trained to remove Gaussian noise from…
Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models
Shirin Shoushtari, Edward P. Chandler, Yuanhao Wang +2
Diffusion models are widely used as priors in imaging inverse problems. However, their performance often degrades under distribution shifts between the training and test-time image…
Closed-Form Approximation of the Total Variation Proximal Operator
Edward P. Chandler, Shirin Shoushtari, Brendt Wohlberg +1
Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constan…
PnP Restoration with Domain Adaptation for SANS
Shirin Shoushtari, Edward P. Chandler, Jialiang Zhang +4
Small Angle Neutron Scattering (SANS) is a non-destructive technique utilized to probe the nano- to mesoscale structure of materials by analyzing the scattering pattern of neutrons…
Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training
Edward P. Chandler, Shirin Shoushtari, Jiaming Liu +2
Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior mo…
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Shirin Shoushtari, Jiaming Liu, Edward P. Chandler +2
Plug-and-Play (PnP) priors is a widely-used family of methods for solving imaging inverse problems by integrating physical measurement models with image priors specified using imag…