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06/29/2023
CLIPAG: Towards Generator-Free Text-to-Image Generation
Perceptually Aligned Gradients (PAG) refer to an intriguing property obs...
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06/04/2023
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
We propose an image restoration algorithm that can control the perceptua...
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05/30/2023
Nested Diffusion Processes for Anytime Image Generation
Diffusion models are the current state-of-the-art in image generation, s...
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05/22/2023
GSURE-Based Diffusion Model Training with Corrupted Data
Diffusion models have demonstrated impressive results in both data gener...
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05/17/2023
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Uncertainty quantification for inverse problems in imaging has drawn muc...
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03/27/2023
Classifier Robustness Enhancement Via Test-Time Transformation
It has been recently discovered that adversarially trained classifiers e...
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02/08/2023
Weakly-supervised Representation Learning for Video Alignment and Analysis
Many tasks in video analysis and understanding boil down to the need for...
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01/09/2023
Image Denoising: The Deep Learning Revolution and Beyond – A Survey Paper –
Image denoising (removal of additive white Gaussian noise from an image)...
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12/06/2022
Towards A Most Probable Recovery in Optical Imaging
Light is a complex-valued field. The intensity and phase of the field ar...
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11/28/2022
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Estimating uncertainty in image-to-image networks is an important task, ...
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11/21/2022
High-Perceptual Quality JPEG Decoding via Posterior Sampling
JPEG is arguably the most popular image coding format, achieving high co...
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11/17/2022
Patch-Craft Self-Supervised Training for Correlated Image Denoising
Supervised neural networks are known to achieve excellent results in var...
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11/16/2022
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
Stochastic restoration algorithms allow to explore the space of solution...
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09/23/2022
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inver...
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08/18/2022
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
Denoising diffusion probabilistic models (DDPMs) are a recent family of ...
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07/22/2022
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
In the past decade, deep learning-based networks have achieved unprecede...
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07/17/2022
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Deep Neural Networks (DNNs) are highly sensitive to imperceptible malici...
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01/27/2022
Denoising Diffusion Restoration Models
Many interesting tasks in image restoration can be cast as linear invers...
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08/08/2021
BIGRoC: Boosting Image Generation via a Robust Classifier
The interest of the machine learning community in image synthesis has gr...
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05/31/2021
SNIPS: Solving Noisy Inverse Problems Stochastically
In this work we introduce a novel stochastic algorithm dubbed SNIPS, whi...
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04/01/2021
Improved Image Generation via Sparse Modeling
The interest of the deep learning community in image synthesis has grown...
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03/25/2021
Patch Craft: Video Denoising by Deep Modeling and Patch Matching
The non-local self-similarity property of natural images has been exploi...
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03/06/2021
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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01/23/2021
Stochastic Image Denoising by Sampling from the Posterior Distribution
Image denoising is a well-known and well studied problem, commonly targe...
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10/14/2020
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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08/03/2020
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study
Handling digital images is almost always accompanied by a lossy compress...
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08/01/2020
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Inverse problems in image processing are typically cast as optimization ...
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02/01/2020
Better Compression with Deep Pre-Editing
Could we compress images via standard codecs while avoiding artifacts? T...
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01/23/2020
Ada-LISTA: Learned Solvers Adaptive to Varying Models
Neural networks that are based on unfolding of an iterative solver, such...
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11/17/2019
Low-Weight and Learnable Image Denoising
Image denoising is a well studied problem with an extensive activity tha...
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09/28/2019
Deep K-SVD Denoising
This work considers noise removal from images, focusing on the well know...
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09/12/2019
Rethinking the CSC Model for Natural Images
Sparse representation with respect to an overcomplete dictionary is ofte...
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03/25/2019
DeepRED: Deep Image Prior Powered by RED
Inverse problems in imaging are extensively studied, with a variety of s...
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11/01/2018
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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06/26/2018
Improving Pursuit Algorithms Using Stochastic Resonance
Sparse Representation Theory is a sub-field of signal processing that ha...
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06/14/2018
Finding GEMS: Multi-Scale Dictionaries for High-Dimensional Graph Signals
Modern data introduces new challenges to classic signal processing appro...
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06/02/2018
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Parsimonious representations in data modeling are ubiquitous and central...
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05/31/2018
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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05/29/2018
Classification Stability for Sparse-Modeled Signals
Despite their impressive performance, deep convolutional neural networks...
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05/06/2018
Acceleration of RED via Vector Extrapolation
Models play an important role in inverse problems, serving as the prior ...
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04/25/2018
Multi Layer Sparse Coding: the Holistic Way
The recently proposed multi-layer sparse model has raised insightful con...
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02/12/2018
Compression for Multiple Reconstructions
In this work we propose a method for optimizing the lossy compression fo...
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01/15/2018
System-Aware Compression
Many information systems employ lossy compression as a crucial intermedi...
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11/21/2017
Optimized Pre-Compensating Compression
In imaging systems, following acquisition, an image/video is transmitted...
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08/29/2017
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) m...
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05/09/2017
Convolutional Dictionary Learning via Local Processing
Convolutional Sparse Coding (CSC) is an increasingly popular model in th...
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02/11/2017
On the Global-Local Dichotomy in Sparsity Modeling
The traditional sparse modeling approach, when applied to inverse proble...
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11/09/2016
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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10/30/2016
Compressed Learning: A Deep Neural Network Approach
Compressed Learning (CL) is a joint signal processing and machine learni...
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09/29/2016