9 citations · 19 across the 3 of their papers we have counts for
11 papers
MetaDIP: Accelerating Deep Image Prior with Meta Learning
Kevin Zhang, Mingyang Xie, Maharshi Gor +3
Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural…
Expectation Consistent Plug-and-Play for MRI
Saurav K Shastri, Rizwan Ahmad, Christopher A Metzler +1
For image recovery problems, plug-and-play (PnP) methods have been developed that replace the proximal step in an optimization algorithm with a call to an application-specific deno…
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…