221 citations · 1.5k across the 104 of their papers we have counts for
62 papers · 1 filter
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…
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…
Deep Parameter Interpolation for Scalar Conditioning
Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona +2
We propose deep parameter interpolation (DPI), a general-purpose method for transforming an existing deep neural network architecture into one that accepts an additional scalar inp…
Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation
Tingjun Liu, Chicago Y. Park, Yuyang Hu +2
Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction by combining diffusion priors with physi…
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…
Plug-and-Play Posterior Sampling for Blind Inverse Problems
Anqi Li, Weijie Gan, Ulugbek S. Kamilov
We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are…