37 citations · 65 across the 7 of their papers we have counts for
10 papers
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems
Bihan Wen, Yanjun Li, Yuqi Li +1
Image prior modeling is the key issue in image recovery, computational imaging, compresses sensing, and other inverse problems. Recent algorithms combining multiple effective prior…
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj, Yoram Bresler, Bo Li
Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples. These examples make deployment of such models in safety-critical tasks qu…
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
Bihan Wen, Saiprasad Ravishankar, Luke Pfister +1
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS)…
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
Ankit Raj, Yuqi Li, Yoram Bresler
A Generative Adversarial Network (GAN) with generator trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed invers…
Optimal Sample Complexity for Stable Matrix Recovery
Yanjun Li, Kiryung Lee, Yoram Bresler
Tremendous efforts have been made to study the theoretical and algorithmic aspects of sparse recovery and low-rank matrix recovery. This paper fills a theoretical gap in matrix rec…
Blind Gain and Phase Calibration via Sparse Spectral Methods
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly…