Search Results for author:
Found 38 papers, 7 papers with code
Gradient Step Denoiser for convergent Plug-and-Play
Exploiting convergence results for proximal gradient descent algorithms in the non-convex setting, we show that the proposed Plug-and-Play algorithm is a convergent iterative scheme that targets stationary points of an explicit global functional.
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
The GOTEX model based on patch features is also adapted to texture inpainting and texture interpolation.
Plug-and-Play image restoration with Stochastic deNOising REgularization
Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Models (DM), where the denoiser is applied only on re-noised images.
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Given this new result, we exploit the convergence theory of proximal algorithms in the nonconvex setting to obtain convergence results for PnP-PGD (Proximal Gradient Descent) and PnP-ADMM (Alternating Direction Method of Multipliers).
Inverse problem regularization with hierarchical variational autoencoders
Our experiments show that the proposed PnP-HVAE method is competitive with both SOTA denoiser-based PnP approaches, and other SOTA restoration methods based on generative models.
Regularized Discrete Optimal Transport
The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes.
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser
This paper presents a new convergent Plug-and-Play (PnP) algorithm.
Regularized Optimal Transport and the Rot Mover's Distance
This paper presents a unified framework for smooth convex regularization of discrete optimal transport problems.
A unified framework for hard and soft clustering with regularized optimal transport
In this paper, we formulate the problem of inferring a Finite Mixture Model from discrete data as an optimal transport problem with entropic regularization of parameter $\lambda\geq 0$.
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought.
Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost
We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory.
Convex Color Image Segmentation with Optimal Transport Distances
This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation.
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.
Texture-Aware Superpixel Segmentation
Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level.
Superpixel-based Color Transfer
In this work, we propose a fast superpixel-based color transfer method (SCT) between two images.
Robust superpixels using color and contour features along linear path
During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter.
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Superpixels have become very popular in many computer vision applications.
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation.
Evaluation Framework of Superpixel Methods with a Global Regularity Measure
To measure the regularity aspect, we propose a new global regularity measure (GR), which addresses the non-robustness of state-of-the-art metrics.
SCALP: Superpixels with Contour Adherence using Linear Path
In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework.
Robust Shape Regularity Criteria for Superpixel Evaluation
Regular decompositions are necessary for most superpixel-based object recognition or tracking applications.
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge.
Learning to segment microscopy images with lazy labels
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
Variational Osmosis for Non-linear Image Fusion
We propose a new variational model for non-linear image fusion.
Multi-task deep learning for image segmentation using recursive approximation tasks
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
Block based refitting in $\ell_{12}$ sparse regularisation
This is done through the use of refitting block penalties that only act on the support of the estimated solution.
On the Existence of Optimal Transport Gradient for Learning Generative Models
Training of WGAN relies on a theoretical background: the calculation of the gradient of the optimal transport cost with respect to the generative model parameters.
Learning local regularization for variational image restoration
In this work, we propose a framework to learn a local regularization model for solving general image restoration problems.
POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring
Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks.
A multi-task U-net for segmentation with lazy labels
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
Diverse super-resolution with pretrained deep hiererarchical VAEs
We investigate the problem of producing diverse solutions to an image super-resolution problem.
A patch-based architecture for multi-label classification from single label annotations
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset.
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
Single-Image based unsupervised joint segmentation and denoising
To this end, we combine the advantages of a variational segmentation method with the power of a self-supervised, single-image based deep learning approach.
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter
The stepsize condition for nonconvex convergence of the proximal algorithm in use then translates to restrictive conditions on the regularization parameter of the inverse problem.
