42 citations · 60 across the 10 of their papers we have counts for
11 papers
The Geometry of Anisotropic Dilation for Optimal Regularization
Carson Newman, Oscar Leong
How can structured transformations of data improve regularizer design for inverse problems? We address this question through anisotropic dilation, a direction-preserving map that r…
Discovering Structure From Corruption for Unsupervised Image Reconstruction
Oscar Leong, Angela F. Gao, He Sun +1
We consider solving ill-posed imaging inverse problems without access to an image prior or ground-truth examples. An overarching challenge in these inverse problems is that an infi…
Image Reconstruction without Explicit Priors
Angela F. Gao, Oscar Leong, He Sun +1
We consider solving ill-posed imaging inverse problems without access to an explicit image prior or ground-truth examples. An overarching challenge in inverse problems is that ther…
Optimal Regularization for a Data Source
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh +1
In optimization-based approaches to inverse problems and to statistical estimation, it is common to augment criteria that enforce data fidelity with a regularizer that promotes des…
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval
Rohun Agrawal, Oscar Leong
Phase retrieval is the nonlinear inverse problem of recovering a true signal from its Fourier magnitude measurements. It arises in many applications such as astronomical imaging, X…
Optimal Sample Complexity of Subgradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration
Paul Hand, Oscar Leong, Vladislav Voroninski
We consider the problem of recovering a real-valued -dimensional signal from phaseless, linear measurements and analyze the amplitude-based non-smooth least squares objectiv…
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…
Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems
Oscar Leong, Wesam Sakla
Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can suc…
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong +2
Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test…
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…
Proving Tucker's Lemma with a Volume Argument
Beauttie Kuture, Oscar Leong, Christopher Loa +2
Sperner's lemma is a statement about labeled triangulations of a simplex. McLennan and Tourky (2007) provided a novel proof of Sperner's Lemma by examining volumes of simplices in…