Search Results for author:
Found 34 papers, 15 papers with code
Neural Approximate Mirror Maps for Constrained Diffusion Models
When the training data all satisfy a certain constraint, enforcing this constraint on a diffusion model not only improves its distribution-matching accuracy but also makes it more reliable for generating valid synthetic data and solving constrained inverse problems.
Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors
Using our Bayesian imaging approach with sophisticated data-driven priors, we can assess how visual features and uncertainty of reconstructed images change depending on the prior.
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems.
Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction
Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality that combines optical absorption contrast with ultrasound imaging depth.
Provable Probabilistic Imaging using Score-Based Generative Priors
PMC is able to incorporate expressive score-based generative priors for high-quality image reconstruction while also performing uncertainty quantification via posterior sampling.
Orbital Polarimetric Tomography of a Flare Near the Sagittarius A* Supermassive Black Hole
One proposed mechanism that produces flares is the formation of compact, bright regions that appear within the accretion disk and close to the event horizon.
Single View Refractive Index Tomography with Neural Fields
The effects of such fields appear in many scientific computer vision settings, ranging from refraction due to transparent cells in microscopy to the lensing of distant galaxies caused by dark matter in astrophysics.
Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior
We demonstrate the surrogate prior on variational inference for efficient approximate posterior sampling of large images.
Learning Task-Specific Strategies for Accelerated MRI
Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance.
Score-Based Diffusion Models as Principled Priors for Inverse Imaging
In this work, we empirically validate the theoretically-proven probability function of a score-based diffusion model.
Discovering Structure From Corruption for Unsupervised Image Reconstruction
We show that such a set of inverse problems can be solved simultaneously without the use of a spatial image prior by instead inferring a shared image generator with a low-dimensional latent space.
Image Reconstruction without Explicit Priors
We show that such a set of inverse problems can be solved simultaneously by learning a shared image generator with a low-dimensional latent space.
Gravitationally Lensed Black Hole Emission Tomography
In this work, we propose BH-NeRF, a novel tomography approach that leverages gravitational lensing to recover the continuous 3D emission field near a black hole.
alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction
Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations.
Unsupervised learning of MRI tissue properties using MRI physics models
In this work we propose an unsupervised deep-learning strategy that employs MRI physics to estimate all three tissue properties from a single multiecho MRI scan session, and generalizes across varying acquisition parameters.
End-to-End Sequential Sampling and Reconstruction for MRI
In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy.
Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements.
Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video
We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations.
Inference of Black Hole Fluid-Dynamics From Sparse Interferometric Measurements
We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging.
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging
In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty.
Learning a Probabilistic Strategy for Computational Imaging Sensor Selection
In this paper, we demonstrate the approach in the context of a very-long-baseline-interferometry (VLBI) array design task, where sensor correlations and atmospheric noise present unique challenges.
Medical Image Imputation from Image Collections
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.
Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
We study the problem of synthesizing a number of likely future frames from a single input image.
Interferometric Imaging Directly with Closure Phases and Closure Amplitudes
Closure-only imaging provides results that are as non-committal as possible and allows for reconstructing an image independently from separate amplitude and phase self-calibration.
Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena
Dynamical Imaging with Interferometry
By linking widely separated radio dishes, the technique of very long baseline interferometry (VLBI) can greatly enhance angular resolution in radio astronomy.
Instrumentation and Methods for Astrophysics
Reconstructing Video from Interferometric Measurements of Time-Varying Sources
Most recently, the Event Horizon Telescope (EHT) has extended VLBI to short millimeter wavelengths with a goal of achieving angular resolution sufficient for imaging the event horizons of nearby supermassive black holes.
Turning Corners Into Cameras: Principles and Methods
We show that walls and other obstructions with edges can be exploited as naturally-occurring "cameras" that reveal the hidden scenes beyond them.
Visual Vibrometry: Estimating Material Properties from Small Motions in Video
The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering.
Visual Vibrometry: Estimating MaterialProperties from Small Motions in Video
The estimation of material properties is important for scene understanding, with many applications in vision, robotics, andstructural engineering.
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy
Consequently, there is an enormous demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution.
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
We study the problem of synthesizing a number of likely future frames from a single input image.
High Resolution Linear Polarimetric Imaging for the Event Horizon Telescope
Polarimetric MEM is thus an attractive choice for image reconstruction with the
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Computational Imaging for VLBI Image Reconstruction
Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth.
Visual Vibrometry: Estimating Material Properties From Small Motion in Video
The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering.
