Search Results for author:
Found 7 papers, 1 papers with code
Super-resolution in disordered media using neural networks
We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media.
Wave-informed dictionary learning for high-resolution imaging in complex media
For these two steps to work together we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain the connectivity needed in the second step.
Dictionary Learning for the Almost-Linear Sparsity Regime
When the dictionary is known, recovery of $\mathbf{x}_i$ is possible even for sparsity linear in dimension $M$, yet to date, the only algorithms which provably succeed in the linear sparsity regime are Riemannian trust-region methods, which are limited to orthogonal dictionaries, and methods based on the sum-of-squares hierarchy, which requires super-polynomial time in order to obtain an error which decays in $M$.
Thresholding Greedy Pursuit for Sparse Recovery Problems
We study here sparse recovery problems in the presence of additive noise.
Fast signal recovery from quadratic measurements
Compared to the sparse signal recovery problem that uses linear measurements, the unknown is now a matrix formed by the cross correlation of the unknown signal.
Imaging with highly incomplete and corrupted data
To improve the performance of $l_1$-minimization we propose to solve instead the augmented linear system $ [A \, | \, C] \rho =b$, where the $N \times \Sigma$ matrix $C$ is a noise collector.
The Noise Collector for sparse recovery in high dimensions
The ability to detect sparse signals from noisy high-dimensional data is a top priority in modern science and engineering.
