
SNIPS: Solving Noisy Inverse Problems Stochastically
In this work we introduce a novel stochastic algorithm dubbed SNIPS, whi...
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Improved Image Generation via Sparse Modeling
The interest of the deep learning community in image synthesis has grown...
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Patch Craft: Video Denoising by Deep Modeling and Patch Matching
The nonlocal selfsimilarity property of natural images has been exploi...
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High Perceptual Quality Image Denoising with a Posterior Sampling CGAN
The vast work in Deep Learning (DL) has led to a leap in image denoising...
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Stochastic Image Denoising by Sampling from the Posterior Distribution
Image denoising is a wellknown and well studied problem, commonly targe...
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Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond
The fields of signal and image processing have been deeply influenced by...
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The RateDistortionAccuracy Tradeoff: JPEG Case Study
Handling digital images is almost always accompanied by a lossy compress...
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Regularization by Denoising via FixedPoint Projection (REDPRO)
Inverse problems in image processing are typically cast as optimization ...
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Better Compression with Deep PreEditing
Could we compress images via standard codecs while avoiding artifacts? T...
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AdaLISTA: Learned Solvers Adaptive to Varying Models
Neural networks that are based on unfolding of an iterative solver, such...
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LowWeight and Learnable Image Denoising
Image denoising is a well studied problem with an extensive activity tha...
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Deep KSVD Denoising
This work considers noise removal from images, focusing on the well know...
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Rethinking the CSC Model for Natural Images
Sparse representation with respect to an overcomplete dictionary is ofte...
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DeepRED: Deep Image Prior Powered by RED
Inverse problems in imaging are extensively studied, with a variety of s...
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A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
The Convolutional Sparse Coding (CSC) model has recently gained consider...
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Improving Pursuit Algorithms Using Stochastic Resonance
Sparse Representation Theory is a subfield of signal processing that ha...
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Finding GEMS: MultiScale Dictionaries for HighDimensional Graph Signals
Modern data introduces new challenges to classic signal processing appro...
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On MultiLayer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Parsimonious representations in data modeling are ubiquitous and central...
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Deep Energy: Using Energy Functions for Unsupervised Training of DNNs
The success of deep learning has been due in no small part to the availa...
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Classification Stability for SparseModeled Signals
Despite their impressive performance, deep convolutional neural networks...
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Acceleration of RED via Vector Extrapolation
Models play an important role in inverse problems, serving as the prior ...
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Multi Layer Sparse Coding: the Holistic Way
The recently proposed multilayer sparse model has raised insightful con...
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Compression for Multiple Reconstructions
In this work we propose a method for optimizing the lossy compression fo...
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SystemAware Compression
Many information systems employ lossy compression as a crucial intermedi...
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Optimized PreCompensating Compression
In imaging systems, following acquisition, an image/video is transmitted...
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MultiLayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
The recently proposed MultiLayer Convolutional Sparse Coding (MLCSC) m...
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Convolutional Dictionary Learning via Local Processing
Convolutional Sparse Coding (CSC) is an increasingly popular model in th...
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On the GlobalLocal Dichotomy in Sparsity Modeling
The traditional sparse modeling approach, when applied to inverse proble...
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The Little Engine that Could: Regularization by Denoising (RED)
Removal of noise from an image is an extensively studied problem in imag...
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Compressed Learning: A Deep Neural Network Approach
Compressed Learning (CL) is a joint signal processing and machine learni...
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StructureAware Classification using Supervised Dictionary Learning
In this paper, we propose a supervised dictionary learning algorithm tha...
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ExampleBased Image Synthesis via Randomized PatchMatching
Image and texture synthesis is a challenging task that has long been dra...
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StyleTransfer via TextureSynthesis
Styletransfer is a process of migrating a style from a given image to t...
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Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Convolutional neural networks (CNN) have led to many stateoftheart re...
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A Deep Learning Approach to Blockbased Compressed Sensing of Images
Compressed sensing (CS) is a signal processing framework for efficiently...
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ConPatch: When a Patch Meets its Context
Measuring the similarity between patches in images is a fundamental buil...
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PatchOrdering as a Regularization for Inverse Problems in Image Processing
Recent work in image processing suggests that operating on (overlapping)...
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Trainlets: Dictionary Learning in High Dimensions
Sparse representations has shown to be a very powerful model for real wo...
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Poisson Inverse Problems by the PlugandPlay scheme
The Anscombe transform offers an approximate conversion of a Poisson ran...
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Simple, Accurate, and Robust Nonparametric Blind SuperResolution
This paper proposes a simple, accurate, and robust approach to single im...
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Boosting of Image Denoising Algorithms
In this paper we propose a generic recursive algorithm for improving ima...
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Bil0l2Norm Regularization for Blind Motion Deblurring
In blind motion deblurring, leading methods today tend towards highly no...
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Sparsity Based Methods for Overparameterized Variational Problems
Two complementary approaches have been extensively used in signal and im...
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SpatiallyAdaptive Reconstruction in Computed Tomography using Neural Networks
We propose a supervised machine learning approach for boosting existing ...
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Sparsity Based Poisson Denoising with Dictionary Learning
The problem of Poisson denoising appears in various imaging applications...
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Image Processing using Smooth Ordering of its Patches
We propose an image processing scheme based on reordering of its patches...
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Redundant Wavelets on Graphs and High Dimensional Data Clouds
In this paper, we propose a new redundant wavelet transform applicable t...
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Generalized TreeBased Wavelet Transform
In this paper we propose a new wavelet transform applicable to functions...
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SpatiallyAdaptive Reconstruction in Computed Tomography Based on Statistical Learning
We propose a direct reconstruction algorithm for Computed Tomography, ba...
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The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods
Linear inverse problems are very common in signal and image processing. ...
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Michael Elad
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Professor in the Computer Science Department of the Technion  Israel Institute of Technology