189 citations · 311 across the 19 of their papers we have counts for
4 papers · 1 filter
D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRI
Christopher A. Metzler, Gordon Wetzstein
Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However,…
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong +1
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand b…
Deep Learning Techniques for Inverse Problems in Imaging
Gregory Ongie, Ajil Jalal, Christopher A. Metzler +3
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central pre…
Deep SPR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
Christopher A. Metzler, Gordon Wetzstein
This paper introduces and solves the simultaneous source separation and phase retrieval (SPR) problem. SPR is an important but largely unsolved problem in a number applicat…