221 citations · 1.5k across the 104 of their papers we have counts for
111 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…
Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling
Chicago Y. Park, Jialin Mao, Xiaojian Xu +3
We introduce DenseAR, a new generative paradigm that reformulates autoregressive image generation as coarse-to-fine next-dense-stride prediction using a compact single-scale tokeni…
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…
A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models
Weijie Gan, Xucheng Wang, Tongyao Wang +6
Image reconstruction and image synthesis are important for handling incomplete multimodal imaging data, but existing methods require various task-specific models, complicating trai…