6 papers
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…
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…
Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matching
An B. Vuong, Michael T. McCann, Javier E. Santos +1
Diffusion models are commonly interpreted as learning the score function, i.e., the gradient of the log-density of noisy data. However, this assumption implies that the target of l…
Random Walks with Tweedie: A Unified View of Score-Based Diffusion Models
Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona +2
We present a concise derivation for several influential score-based diffusion models that relies on only a few textbook results. Diffusion models have recently emerged as powerful…
An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM
Luke Lozenski, Michael T. McCann, Brendt Wohlberg
This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization pro…
Plug-and-Play Priors as a Score-Based Method
Chicago Y. Park, Yuyang Hu, Michael T. McCann +3
Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-ba…