1 citations · 1 across the 3 of their papers we have counts for
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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…
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…
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…
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…
Learned Correction Methods for Ultrasound Computed Tomography Imaging Using Simplified Physics Models
Luke Lozenski, Hanchen Wang, Fu Li +4
Ultrasound computed tomography (USCT) is an emerging modality for breast imaging. Image reconstruction methods that incorporate accurate wave physics produce high resolution quanti…