162 citations · 164 across the 2 of their papers we have counts for
5 papers
Accelerating Least Squares Imaging Using Deep Learning Techniques
Janaki Vamaraju, Jeremy Vila, Mauricio Araya-Polo +3
Wave equation techniques have been an integral part of geophysical imaging workflows to investigate the Earth's subsurface. Least-squares reverse time migration (LSRTM) is a linear…
Hyperspectral Unmixing via Turbo Bilinear Approximate Message Passing
Jeremy Vila, Philip Schniter, Joseph Meola
The goal of hyperspectral unmixing is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels into N constituent material spectra (or "end-memb…
Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm
Jeremy Vila, Philip Schniter, Sundeep Rangan +2
The generalized approximate message passing (GAMP) algorithm is an efficient method of MAP or approximate-MMSE estimation of observed from a noisy version of the transform coef…
Generalized Approximate Message Passing for Cosparse Analysis Compressive Sensing
Mark Borgerding, Philip Schniter, Sundeep Rangan
In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given…
An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals
Jeremy Vila, Philip Schniter
We propose two novel approaches to the recovery of an (approximately) sparse signal from noisy linear measurements in the case that the signal is a priori known to be non-negative…