6 citations · 14 across the 18 of their papers we have counts for
21 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…
EigenScore: OOD Detection using Covariance in Diffusion Models
Shirin Shoushtari, Yi Wang, Xiao Shi +2
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems in safety-sensitive domains. Diffusion models have recently emerged as powerful…
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…
Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problems
Yuanhao Wang, Shirin Shoushtari, Ulugbek S. Kamilov
Diffusion models are extensively used for modeling image priors for inverse problems. We introduce \emph{Diff-Unfolding}, a principled framework for learning posterior score functi…
Measurement Score-Based Diffusion Model
Chicago Y. Park, Shirin Shoushtari, Hongyu An +1
Diffusion models are widely used in applications ranging from image generation to inverse problems. However, training diffusion models typically requires clean ground-truth images,…
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…
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy
Zihao Zou, Shirin Shoushtari, Jiaming Liu +6
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signa…
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…
Convergence of Nonconvex PnP-ADMM with MMSE Denoisers
Chicago Park, Shirin Shoushtari, Weijie Gan +1
Plug-and-Play Alternating Direction Method of Multipliers (PnP-ADMM) is a widely-used algorithm for solving inverse problems by integrating physical measurement models and convolut…
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Zihao Zou, Jiaming Liu, Shirin Shoushtari +3
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input. While diffusion probab…
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…
Block Coordinate Plug-and-Play Methods for Blind Inverse Problems
Weijie Gan, Shirin Shoushtari, Yuyang Hu +3
Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and lea…
DOLPH: Diffusion Models for Phase Retrieval
Shirin Shoushtari, Jiaming Liu, Ulugbek S. Kamilov
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires pr…
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Junhao Hu, Shirin Shoushtari, Zihao Zou +3
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and…
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
Shirin Shoushtari, Jiaming Liu, Yuyang Hu +1
There is a growing interest in deep model-based architectures (DMBAs) for solving imaging inverse problems by combining physical measurement models and learned image priors specifi…
Online Deep Equilibrium Learning for Regularization by Denoising
Jiaming Liu, Xiaojian Xu, Weijie Gan +2
Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining ph…