Search Results for author: Yoram Bresler

Found 19 papers, 2 papers with code

Scatter Correction in X-ray CT by Physics-Inspired Deep Learning

no code implementations21 Mar 2021 Berk Iskender, Yoram Bresler

Different from previous works, they incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it, and use a deep neural network architecture and cost function, both specifically tailored to the problem.

Computed Tomography (CT)

Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering

no code implementations23 Jan 2021 Ufuk Soylu, Yoram Bresler

We believe that the proposed velocity filtering method has the potential to pave the way to clinical translation of ULM.

Super-Resolution Translation

Joint Dimensionality Reduction for Separable Embedding Estimation

no code implementations14 Jan 2021 Yanjun Li, Bihan Wen, Hao Cheng, Yoram Bresler

In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of different modalities or data from distinct types of entities.

Information Retrieval Supervised dimensionality reduction

A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems

no code implementations29 Mar 2020 Bihan Wen, Yanjun Li, Yuqi Li, Yoram Bresler

Furthermore, we relate the denoising performance improvement by combining multiple models, to the image model relationships.

Denoising Image Restoration

Improving Robustness of Deep-Learning-Based Image Reconstruction

no code implementations ICML 2020 Ankit Raj, Yoram Bresler, Bo Li

We find that a linear network using the proposed min-max learning scheme indeed converges to the same solution.

Image Reconstruction

GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems

no code implementations ICCV 2019 Ankit Raj, Yuqi Li, Yoram Bresler

A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems.


Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere

no code implementations NeurIPS 2018 Yanjun Li, Yoram Bresler

Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from convolutional measurements $y_i=x_i \circledast f$ ($i=1, 2,\dots, N$).

The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling

no code implementations3 Aug 2018 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.

Dictionary Learning Image Denoising +2

Multichannel Sparse Blind Deconvolution on the Sphere

no code implementations NeurIPS 2018 Yanjun Li, Yoram Bresler

Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from their circular convolution $y_i=x_i \circledast f$ ($i=1, 2,\dots, N$).

Learning Filter Bank Sparsifying Transforms

no code implementations6 Mar 2018 Luke Pfister, Yoram Bresler

We show that many existing transform and analysis sparse representations can be viewed as filter banks, thus linking the local properties of patch-based model to the global properties of a convolutional model.

Image Denoising

Blind Gain and Phase Calibration via Sparse Spectral Methods

no code implementations30 Nov 2017 Yanjun Li, Kiryung Lee, Yoram Bresler

We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e. g., with noisy measurement or with a bad initial estimate.

VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising

1 code implementation3 Oct 2017 Bihan Wen, Saiprasad Ravishankar, Yoram Bresler

Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.

Dictionary Learning Image Denoising +2

Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising

1 code implementation ICCV 2017 Bihan Wen, Yanjun Li, Luke Pfister, Yoram Bresler

In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT.

Denoising Video Denoising

Joint Dimensionality Reduction for Two Feature Vectors

no code implementations13 Feb 2016 Yanjun Li, Yoram Bresler

This paper addresses the joint dimensionality reduction of two feature vectors in supervised learning problems.

Dimensionality Reduction General Classification

FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications

no code implementations19 Nov 2015 Bihan Wen, Saiprasad Ravishankar, Yoram Bresler

Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision.

Denoising Dictionary Learning +1

Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing

no code implementations4 Nov 2015 Saiprasad Ravishankar, Yoram Bresler

In this work, we focus on blind compressed sensing (BCS), where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements.

Image Reconstruction

Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI

no code implementations13 Jan 2015 Saiprasad Ravishankar, Yoram Bresler

Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT.

Image Reconstruction

$\ell_0$ Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees

no code implementations13 Jan 2015 Saiprasad Ravishankar, Yoram Bresler

Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary.

Image Denoising Image Reconstruction

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