187 citations · 705 across the 37 of their papers we have counts for
39 papers
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging
Berk Iskender, Sushan Nakarmi, Nitin Daphalapurkar +2
Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only…
Learned Bayesian Cramér-Rao Bound for Unknown Measurement Models Using Score Neural Networks
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Bayesian Cramér-Rao bound (BCRB) is a crucial tool in signal processing for assessing the fundamental limitations of any estimation problem as well as benchmarking within a Bay…
RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging
Berk Iskender, Marc L. Klasky, Yoram Bresler
Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography,…
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
Berk Iskender, Marc L. Klasky, Yoram Bresler
In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly…
Learning to Bound: A Generative Cramér-Rao Bound
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Cramér-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain t…
Scatter Correction in X-ray CT by Physics-Inspired Deep Learning
Berk Iskender, Yoram Bresler
Scatter due to interaction of photons with the imaged object is a fundamental problem in X-ray Computed Tomography (CT). It manifests as various artifacts in the reconstruction, ma…
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering
Ufuk Soylu, Yoram Bresler
Ultrasound Localization Microscopy (ULM) offers a cost-effective modality for microvascular imaging by using intravascular contrast agents (microbubbles). However, ULM has a fundam…
Joint Dimensionality Reduction for Separable Embedding Estimation
Yanjun Li, Bihan Wen, Hao Cheng +1
Low-dimensional embeddings for data from disparate sources play critical roles in multi-modal machine learning, multimedia information retrieval, and bioinformatics. In this paper,…
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…
Composition-Aware Spectroscopic Tomography
Luke Pfister, Rohit Bhargava, Yoram Bresler +1
Chemical imaging provides information about the distribution of chemicals within a target. When combined with structural information about the target, in situ chemical imaging open…
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…
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling
Bihan Wen, Yanjun Li, Yoram Bresler
Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications. Patch-based methods exploit loc…
Multichannel Sparse Blind Deconvolution on the Sphere
Yanjun Li, Yoram Bresler
Multichannel blind deconvolution is the problem of recovering an unknown signal and multiple unknown channels from their circular convolution ($i=…
Learning Filter Bank Sparsifying Transforms
Luke Pfister, Yoram Bresler
Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have be…
Bounding Multivariate Trigonometric Polynomials with Applications to Filter Bank Design
Luke Pfister, Yoram Bresler
The extremal values of multivariate trigonometric polynomials are of interest in fields ranging from control theory to filter design, but finding the extremal values of such a poly…
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…
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Techniques exploiting the sparsity of images in a transform domain have been effective for various applications in image and video processing. Transform learning methods involve ch…
Joint Dimensionality Reduction for Two Feature Vectors
Yanjun Li, Yoram Bresler
Many machine learning problems, especially multi-modal learning problems, have two sets of distinct features (e.g., image and text features in news story classification, or neuroim…
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…
Optimal Sample Complexity for Blind Gain and Phase Calibration
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind gain and phase calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo…
Blind Recovery of Sparse Signals from Subsampled Convolution
Kiryung Lee, Yanjun Li, Marius Junge +1
Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors ta…