17 citations · 30 across the 10 of their papers we have counts for
5 papers · 1 filter
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors
Paul Hand, Oscar Leong, Vladislav Voroninski
Advances in compressive sensing provided reconstruction algorithms of sparse signals from linear measurements with optimal sample complexity, but natural extensions of this methodo…
Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming
Ali Ahmed, Alireza Aghasi, Paul Hand
We consider the task of recovering two real or complex -vectors from phaseless Fourier measurements of their circular convolution. Our method is a novel convex relaxation that i…
Phase Retrieval Under a Generative Prior
Paul Hand, Oscar Leong, Vladislav Voroninski
The phase retrieval problem asks to recover a natural signal from quadratic observations, where is to be minimized. As is common in many imaging prob…
Blind Deconvolutional Phase Retrieval via Convex Programming
Ali Ahmed, Alireza Aghasi, Paul Hand
We consider the task of recovering two real or complex -vectors from phaseless Fourier measurements of their circular convolution. Our method is a novel convex relaxation that i…
Rate-Optimal Denoising with Deep Neural Networks
Reinhard Heckel, Wen Huang, Paul Hand +1
Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying princip…