NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems
Bayesian methods for solving inverse problems are a powerful alternative to classical
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …
Multi-scale energy (MuSE) plug and play framework for inverse problems
We introduce a multi-scale energy formulation for plug and play (PnP) image recovery. The
main highlight of the proposed framework is energy formulation, where the log prior of the …
main highlight of the proposed framework is energy formulation, where the log prior of the …
NF-ULA: Langevin Monte Carlo with normalizing flow prior for imaging inverse problems
Bayesian methods for solving inverse problems are a powerful alternative to classical
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …
The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse
problems from large data volumes. Recent image reconstruction techniques grounded in …
problems from large data volumes. Recent image reconstruction techniques grounded in …
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers
The spatial resolution of images of living samples obtained by fluorescence microscopes is
physically limited due to the diffraction of visible light, which makes the study of entities of …
physically limited due to the diffraction of visible light, which makes the study of entities of …
Coordinate-based seismic interpolation in irregular land survey: a deep internal learning approach
Physical and budget constraints often result in irregular sampling, which complicates
accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot …
accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot …
Deep model-based architectures for inverse problems under mismatched priors
There is a growing interest in deep model-based architectures (DMBAs) for solving imaging
inverse problems by combining physical measurement models and learned image priors …
inverse problems by combining physical measurement models and learned image priors …
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Plug-and-Play (PnP) priors is a widely-used family of methods for solving imaging inverse
problems by integrating physical measurement models with image priors specified using …
problems by integrating physical measurement models with image priors specified using …
RED-PSM: Regularization by denoising of partially separable models for dynamic imaging
B Iskender, ML Klasky… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Dynamic imaging involves the recovery of a time-varying 2D or 3D object at each time
instant using its undersampled measurements. In particular, in dynamic tomography, only a …
instant using its undersampled measurements. In particular, in dynamic tomography, only a …
What's in a Prior? Learned Proximal Networks for Inverse Problems
Proximal operators are ubiquitous in inverse problems, commonly appearing as part of
algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep …
algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep …