Search Results for author:
Found 68 papers, 32 papers with code
CLIPAG: Towards Generator-Free Text-to-Image Generation
Perceptually Aligned Gradients (PAG) refer to an intriguing property observed in robust image classification models, wherein their input gradients align with human perception and pose semantic meanings.
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
We propose an image restoration algorithm that can control the perceptual quality and/or the mean square error (MSE) of any pre-trained model, trading one over the other at test time.
Nested Diffusion Processes for Anytime Image Generation
Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising steps.
GSURE-Based Diffusion Model Training with Corrupted Data
Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more.
Semi-supervised Quality Evaluation of Colonoscopy Procedures
By integrating the local metric over the withdrawal phase, we build a global, offline quality metric, which is shown to be highly correlated to the standard Polyp Per Colonoscopy (PPC) quality metric.
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Uncertainty quantification for inverse problems in imaging has drawn much attention lately.
Classifier Robustness Enhancement Via Test-Time Transformation
We show that the proposed method achieves state-of-the-art results and validate our claim through extensive experiments on a variety of defense methods, classifier architectures, and datasets.
Weakly-supervised Representation Learning for Video Alignment and Analysis
This paper introduces LRProp -- a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category.
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --
Our aim is to give a better context to recent discoveries, and to the influence of DL in our domain.
Complex-valued Retrievals From Noisy Images Using Diffusion Models
In this study, we generalize annealed Langevin Dynamics, a type of DDM, to tackle the fundamental challenges in optical imaging of complex-valued objects (and real images) affected by Poisson noise.
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Our approach is agnostic to the underlying image-to-image network, and only requires triples of the input (degraded), reconstructed and true images for training.
High-Perceptual Quality JPEG Decoding via Posterior Sampling
JPEG is arguably the most popular image coding format, achieving high compression ratios via lossy quantization that may create visual artifacts degradation.
Patch-Craft Self-Supervised Training for Correlated Image Denoising
Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training.
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input.
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inverse problems.
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
In order to obtain class-conditional generation, it was suggested to guide the diffusion process by gradients from a time-dependent classifier.
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
In this work, we focus on this trait and test whether \emph{Perceptually Aligned Gradients imply Robustness}.
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks.
Denoising Diffusion Restoration Models
Many interesting tasks in image restoration can be cast as linear inverse problems.
Improved Image Generation via Sparsity
The interest of the deep learning community in image synthesis has grown massively in recent years.
BIGRoC: Boosting Image Generation via a Robust Classifier
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.
Ranked #4 on
Image Generation
on ImageNet 128x128
SNIPS: Solving Noisy Inverse Problems Stochastically
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.
Improved Image Generation via Sparse Modeling
The interest of the deep learning community in image synthesis has grown massively in recent years.
Patch Craft: Video Denoising by Deep Modeling and Patch Matching
Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN.
Ranked #4 on
Color Image Denoising
on CBSD68 sigma25
High Perceptual Quality Image Denoising with a Posterior Sampling CGAN
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.
Stochastic Image Denoising by Sampling from the Posterior Distribution
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.
On the Inversion of Deep Generative Models
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
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
As a case study, we focus on the design of the quantization tables in the JPEG compression standard.
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED).
When and How Can Deep Generative Models be Inverted?
Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.
Better Compression with Deep Pre-Editing
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
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.
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation
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.
Deep K-SVD Denoising
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.
Rethinking the CSC Model for Natural Images
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing.
Ranked #1 on
Color Image Denoising
on BSD68 sigma75
DeepRED: Deep Image Prior Powered by RED
Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years.
Ranked #7 on
Image Super-Resolution
on Set14 - 8x upscaling
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss
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.
Ranked #14 on
Image Dehazing
on SOTS Outdoor
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities.
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance
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
Modern data introduces new challenges to classic signal processing approaches, leading to a growing interest in the field of graph signal processing.
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Parsimonious representations are ubiquitous in modeling and processing information.
Deep-Energy: Unsupervised Training of Deep Neural Networks
The success of deep learning has been due, in no small part, to the availability of large annotated datasets.
Ranked #15 on
Image Dehazing
on SOTS Outdoor
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals
Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations.
Acceleration of RED via Vector Extrapolation
Models play an important role in inverse problems, serving as the prior for representing the original signal to be recovered.
Multi-Layer Sparse Coding: The Holistic Way
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
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.
Convolutional Dictionary Learning via Local Processing
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.
On the Global-Local Dichotomy in Sparsity Modeling
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)
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.
Compressed Learning: A Deep Neural Network Approach
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.
Structure-Aware Classification using Supervised Dictionary Learning
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data.
Example-Based Image Synthesis via Randomized Patch-Matching
Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning.
Style-Transfer via Texture-Synthesis
Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained.
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
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
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
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.
Patch-Ordering as a Regularization for Inverse Problems in Image Processing
Recent work in image processing suggests that operating on (overlapping) patches in an image may lead to state-of-the-art results.
Trainlets: Dictionary Learning in High Dimensions
Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance.
Poisson Inverse Problems by the Plug-and-Play scheme
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".
Postprocessing of Compressed Images via Sequential Denoising
In this work we propose a novel postprocessing technique for compression-artifact reduction.
Linearized Kernel Dictionary Learning
In this paper we present a new approach of incorporating kernels into dictionary learning.
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution
This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR).
Boosting of Image Denoising Algorithms
In this paper we propose a generic recursive algorithm for improving image denoising methods.
Bi-l0-l2-Norm Regularization for Blind Motion Deblurring
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.
Sparsity Based Methods for Overparameterized Variational Problems
Two complementary approaches have been extensively used in signal and image processing leading to novel results, the sparse representation methodology and the variational strategy.
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
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.
Sparsity Based Poisson Denoising with Dictionary Learning
In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive i. i. d.
