Search Results for author: Michael Elad

Found 50 papers, 22 papers with code

Denoising Diffusion Restoration Models

1 code implementation27 Jan 2022 Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song

Many interesting tasks in image restoration can be cast as linear inverse problems.

Colorization Deblurring +4

Improved Image Generation via Sparsity

no code implementations29 Sep 2021 Roy Ganz, Michael Elad

The interest of the deep learning community in image synthesis has grown massively in recent years.

Image Generation

BIGRoC: Boosting Image Generation via a Robust Classifier

1 code implementation8 Aug 2021 Roy Ganz, Michael Elad

The interest of the machine learning community in image synthesis has grown significantly in recent years, with the introduction of a wide range of deep generative models and means for training them.

Image Generation

SNIPS: Solving Noisy Inverse Problems Stochastically

1 code implementation NeurIPS 2021 Bahjat Kawar, Gregory Vaksman, Michael Elad

In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is assumed to be contaminated by additive white Gaussian noise.

Compressive Sensing Deblurring +2

Improved Image Generation via Sparse Modeling

no code implementations1 Apr 2021 Roy Ganz, Michael Elad

The interest of the deep learning community in image synthesis has grown massively in recent years.

Image Generation

High Perceptual Quality Image Denoising with a Posterior Sampling CGAN

1 code implementation6 Mar 2021 Guy Ohayon, Theo Adrai, Gregory Vaksman, Michael Elad, Peyman Milanfar

We showcase our proposed method with a novel denoiser architecture that achieves the reformed denoising goal and produces vivid and diverse outcomes in immoderate noise levels.

Image Denoising

Stochastic Image Denoising by Sampling from the Posterior Distribution

no code implementations23 Jan 2021 Bahjat Kawar, Gregory Vaksman, Michael Elad

Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image.

Image Denoising

On the Inversion of Deep Generative Models

no code implementations1 Jan 2021 Aviad Aberdam, Dror Simon, Michael Elad

Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.

Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond

1 code implementation14 Oct 2020 Rajaei Khatib, Dror Simon, Michael Elad

A popular representative of this approach is the Iterative Shrinkage-Thresholding Algorithm (ISTA) and its learned version -- LISTA, aiming for the sparse representations of the processed signals.

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

no code implementations3 Aug 2020 Xiyang Luo, Hossein Talebi, Feng Yang, Michael Elad, Peyman Milanfar

As a case study, we focus on the design of the quantization tables in the JPEG compression standard.

Quantization

Regularization by Denoising via Fixed-Point Projection (RED-PRO)

no code implementations1 Aug 2020 Regev Cohen, Michael Elad, Peyman Milanfar

Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED).

Deblurring Denoising +2

When and How Can Deep Generative Models be Inverted?

no code implementations28 Jun 2020 Aviad Aberdam, Dror Simon, Michael Elad

Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.

Better Compression with Deep Pre-Editing

no code implementations1 Feb 2020 Hossein Talebi, Damien Kelly, Xiyang Luo, Ignacio Garcia Dorado, Feng Yang, Peyman Milanfar, Michael Elad

In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits.

Ada-LISTA: Learned Solvers Adaptive to Varying Models

1 code implementation23 Jan 2020 Aviad Aberdam, Alona Golts, Michael Elad

Neural networks that are based on unfolding of an iterative solver, such as LISTA (learned iterative soft threshold algorithm), are widely used due to their accelerated performance.

Image Denoising Image Inpainting

LIDIA: Lightweight Learned Image Denoising with Instance Adaptation

1 code implementation17 Nov 2019 Gregory Vaksman, Michael Elad, Peyman Milanfar

This work proposes a novel lightweight learnable architecture for image denoising, and presents a combination of supervised and unsupervised training of it, the first aiming for a universal denoiser and the second for adapting it to the incoming image.

Grayscale Image Denoising Image Denoising

Deep K-SVD Denoising

no code implementations28 Sep 2019 Meyer Scetbon, Michael Elad, Peyman Milanfar

The question we address in this paper is whether K-SVD was brought to its peak in its original conception, or whether it can be made competitive again.

Denoising

Rethinking the CSC Model for Natural Images

1 code implementation NeurIPS 2019 Dror Simon, Michael Elad

Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing.

Color Image Denoising

DeepRED: Deep Image Prior Powered by RED

1 code implementation25 Mar 2019 Gary Mataev, Michael Elad, Peyman Milanfar

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years.

Deblurring Denoising +1

Unsupervised Single Image Dehazing Using Dark Channel Prior Loss

1 code implementation6 Dec 2018 Alona Golts, Daniel Freedman, Michael Elad

Instead of feeding the network with synthetic data, we solely use real-world outdoor images and tune the network's parameters by directly minimizing the DCP.

Image Dehazing Single Image Dehazing

A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model

2 code implementations1 Nov 2018 Ev Zisselman, Jeremias Sulam, Michael Elad

The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities.

Image Inpainting online learning

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance

no code implementations26 Jun 2018 Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad

The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal.

Finding GEMS: Multi-Scale Dictionaries for High-Dimensional Graph Signals

no code implementations14 Jun 2018 Yael Yankelevsky, Michael Elad

Modern data introduces new challenges to classic signal processing approaches, leading to a growing interest in the field of graph signal processing.

Dictionary Learning

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks

2 code implementations2 Jun 2018 Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad

Parsimonious representations are ubiquitous in modeling and processing information.

Deep-Energy: Unsupervised Training of Deep Neural Networks

1 code implementation31 May 2018 Alona Golts, Daniel Freedman, Michael Elad

The success of deep learning has been due, in no small part, to the availability of large annotated datasets.

Image Dehazing Image Matting +1

Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals

no code implementations29 May 2018 Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad

Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations.

General Classification

Acceleration of RED via Vector Extrapolation

1 code implementation6 May 2018 Tao Hong, Yaniv Romano, Michael Elad

Models play an important role in inverse problems, serving as the prior for representing the original signal to be recovered.

Denoising

Multi-Layer Sparse Coding: The Holistic Way

no code implementations25 Apr 2018 Aviad Aberdam, Jeremias Sulam, Michael Elad

The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and convolutional neural networks (CNN).

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

no code implementations29 Aug 2017 Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad

We show that the training of the filters is essential to allow for non-trivial signals in the model, and we derive an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers.

Dictionary Learning

Convolutional Dictionary Learning via Local Processing

1 code implementation ICCV 2017 Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad

Convolutional Sparse Coding (CSC) is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based sparse representations.

Dictionary Learning Image Inpainting

On the Global-Local Dichotomy in Sparsity Modeling

no code implementations11 Feb 2017 Dmitry Batenkov, Yaniv Romano, Michael Elad

The traditional sparse modeling approach, when applied to inverse problems with large data such as images, essentially assumes a sparse model for small overlapping data patches.

The Little Engine that Could: Regularization by Denoising (RED)

2 code implementations9 Nov 2016 Yaniv Romano, Michael Elad, Peyman Milanfar

As opposed to the $P^3$ method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem.

Deblurring Image Deblurring +2

Compressed Learning: A Deep Neural Network Approach

2 code implementations30 Oct 2016 Amir Adler, Michael Elad, Michael Zibulevsky

Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projections of the signal.

General Classification Image Classification

Structure-Aware Classification using Supervised Dictionary Learning

no code implementations29 Sep 2016 Yael Yankelevsky, Michael Elad

In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data.

Classification Dictionary Learning +2

Example-Based Image Synthesis via Randomized Patch-Matching

no code implementations23 Sep 2016 Yi Ren, Yaniv Romano, Michael Elad

Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning.

Image Generation Patch Matching +1

Style-Transfer via Texture-Synthesis

2 code implementations10 Sep 2016 Michael Elad, Peyman Milanfar

Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained.

Style Transfer Texture Synthesis

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

no code implementations27 Jul 2016 Vardan Papyan, Yaniv Romano, Michael Elad

This is shown to be tightly connected to CNN, so much so that the forward pass of the CNN is in fact the thresholding pursuit serving the ML-CSC model.

A Deep Learning Approach to Block-based Compressed Sensing of Images

1 code implementation5 Jun 2016 Amir Adler, David Boublil, Michael Elad, Michael Zibulevsky

Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.

Con-Patch: When a Patch Meets its Context

no code implementations22 Mar 2016 Yaniv Romano, Michael Elad

Therefore, with a minor increase of the dimensions (e. g. with additional 10 values to the patch representation), we implicitly/softly describe the information of a large patch.

Image Denoising Image Super-Resolution

Patch-Ordering as a Regularization for Inverse Problems in Image Processing

1 code implementation26 Feb 2016 Gregory Vaksman, Michael Zibulevsky, Michael Elad

Recent work in image processing suggests that operating on (overlapping) patches in an image may lead to state-of-the-art results.

Deblurring Image Deblurring +4

Trainlets: Dictionary Learning in High Dimensions

no code implementations31 Jan 2016 Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad

Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance.

Dictionary Learning

Poisson Inverse Problems by the Plug-and-Play scheme

no code implementations8 Nov 2015 Arie Rond, Raja Giryes, Michael Elad

In this work we suggest a novel method for coupling Gaussian denoising algorithms to Poisson noisy inverse problems, which is based on a general approach termed "Plug-and-Play".

Denoising

Postprocessing of Compressed Images via Sequential Denoising

no code implementations30 Oct 2015 Yehuda Dar, Alfred M. Bruckstein, Michael Elad, Raja Giryes

In this work we propose a novel postprocessing technique for compression-artifact reduction.

Image Denoising

Linearized Kernel Dictionary Learning

1 code implementation18 Sep 2015 Alona Golts, Michael Elad

In this paper we present a new approach of incorporating kernels into dictionary learning.

Dictionary Learning General Classification

Simple, Accurate, and Robust Nonparametric Blind Super-Resolution

no code implementations11 Mar 2015 Wen-Ze Shao, Michael Elad

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR).

Blind Super-Resolution Super-Resolution

Boosting of Image Denoising Algorithms

no code implementations22 Feb 2015 Yaniv Romano, Michael Elad

In this paper we propose a generic recursive algorithm for improving image denoising methods.

Image Denoising

Bi-l0-l2-Norm Regularization for Blind Motion Deblurring

no code implementations20 Aug 2014 Wen-Ze Shao, Hai-Bo Li, Michael Elad

In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel.

Deblurring

Sparsity Based Methods for Overparameterized Variational Problems

no code implementations20 May 2014 Raja Giryes, Michael Elad, Alfred M. Bruckstein

Two complementary approaches have been extensively used in signal and image processing leading to novel results, the sparse representation methodology and the variational strategy.

Denoising Optical Flow Estimation

Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks

no code implementations28 Nov 2013 Joseph Shtok, Michael Zibulevsky, Michael Elad

We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography.

Image Reconstruction

Sparsity Based Poisson Denoising with Dictionary Learning

no code implementations17 Sep 2013 Raja Giryes, Michael Elad

In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive i. i. d.

Denoising Dictionary Learning

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