189 citations · 311 across the 19 of their papers we have counts for
22 papers
Snapshot High Dynamic Range Imaging with a Polarization Camera
Mingyang Xie, Matthew Chan, Christopher Metzler
High dynamic range (HDR) images are important for a range of tasks, from navigation to consumer photography. Accordingly, a host of specialized HDR sensors have been developed, the…
Seeing the World through Your Eyes
Hadi Alzayer, Kevin Zhang, Brandon Feng +2
The reflective nature of the human eye is an underappreciated source of information about what the world around us looks like. By imaging the eyes of a moving person, we can collec…
SUD: Supervision by Denoising Diffusion Models for Image Reconstruction
Matthew A. Chan, Sean I. Young, Christopher A. Metzler
Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on…
TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions
Sachin Shah, Sakshum Kulshrestha, Christopher A. Metzler
Point-spread-function (PSF) engineering is a powerful computational imaging techniques wherein a custom phase mask is integrated into an optical system to encode additional informa…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery
Rushil Joshi, Ethan Adams, Matthew Ziemann +1
The United States coastline spans 95,471 miles; a distance that cannot be effectively patrolled or secured by manual human effort alone. Unmanned Aerial Vehicles (UAVs) equipped wi…
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…
Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery
Saurav K. Shastri, Rizwan Ahmad, Christopher A. Metzler +1
To solve inverse problems, plug-and-play (PnP) methods replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implement…
TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence
Brandon Yushan Feng, Mingyang Xie, Christopher A. Metzler
We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. Our approach requires no paired training data, ada…
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…
Supervision by Denoising for Medical Image Segmentation
Sean I. Young, Adrian V. Dalca, Enzo Ferrante +4
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging doma…
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…
Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path
Christopher A. Metzler, David B. Lindell, Gordon Wetzstein
Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transie…
Deep Optics for Single-shot High-dynamic-range Imaging
Christopher A. Metzler, Hayato Ikoma, Yifan Peng +1
High-dynamic-range (HDR) imaging is crucial for many computer graphics and vision applications. Yet, acquiring HDR images with a single shot remains a challenging problem. Whereas…
An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing
Christopher A. Metzler, Philip Schniter, Richard G. Baraniuk
Generalized Vector Approximate Message Passing (GVAMP) is an efficient iterative algorithm for approximately minimum-mean-squared-error estimation of a random vector $\mathbf{x}\si…
Unsupervised Learning with Stein's Unbiased Risk Estimator
Christopher A. Metzler, Ali Mousavi, Reinhard Heckel +1
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this w…
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan +1
Phase retrieval algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation ima…
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk
Compressive image recovery is a challenging problem that requires fast and accurate algorithms. Recently, neural networks have been applied to this problem with promising results.…
From Denoising to Compressed Sensing
Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk
A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a resul…