181 citations · 396 across the 31 of their papers we have counts for
6 papers · 1 filter
A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors
Wen Huang, Paul Hand, Reinhard Heckel +1
Deep generative modeling has led to new and state of the art approaches for enforcing structural priors in a variety of inverse problems. In contrast to priors given by sparsity, d…
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Reinhard Heckel, Paul Hand
Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpai…
A convex program for bilinear inversion of sparse vectors
Alireza Aghasi, Ali Ahmed, Paul Hand +1
We consider the bilinear inverse problem of recovering two vectors, and , from their entrywise product. We consider t…
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…