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Article
Convergence analysis of block majorize-minimize subspace approach
We consider the minimization of a differentiable Lipschitz gradient but non necessarily convex, function F defined on \({\mathbb {R}}^N\) ...
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Article
A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially Varying Generalised Gaussian Models
The joint problem of reconstruction/feature extraction is a challenging task in image processing. It consists in performing, in a joint manner, the restoration of an image and the extraction of its features. I...
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Article
Deep learning for automatic bowel-obstruction identification on abdominal CT
Automated evaluation of abdominal computed tomography (CT) scans should help radiologists manage their massive workloads, thereby leading to earlier diagnoses and better patient outcomes. Our objective was to ...
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Article
A Local MM Subspace Method for Solving Constrained Variational Problems in Image Recovery
This article introduces a new penalized majorization–minimization subspace algorithm (P-MMS) for solving smooth, constrained optimization problems. In short, our approach consists of embedding a subspace algor...
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Article
SABRINA: A Stochastic Subspace Majorization-Minimization Algorithm
A wide class of problems involves the minimization of a coercive and differentiable function F on \({\mathbb {R}}^N\) ...
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Article
Open AccessA computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials
The year 2020 witnessed a heavy death toll due to COVID-19, calling for a global emergency. The continuous ongoing research and clinical trials paved the way for vaccines. But, the vaccine efficacy in the long...
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Article
Open AccessIntegrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients
The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from...
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Article
A Proximal Interior Point Algorithm with Applications to Image Processing
In this article, we introduce a new proximal interior point algorithm (PIPA). This algorithm is able to handle convex optimization problems involving various constraints where the objective function is the sum...
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Article
Open AccessDeConFuse: a deep convolutional transform-based unsupervised fusion framework
This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of co...
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Chapter and Conference Paper
Deep Convolutional Transform Learning
This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL). By stacking convolutional transforms, our approach is able to learn a set of indep...
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Article
Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging
Fitting Gaussian functions to empirical data is a crucial task in a variety of scientific applications, especially in image processing. However, most of the existing approaches for performing such fitting are ...
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Article
A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression
In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas–Rachford splitting method. Our algorithm performs both function and variable s...
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Chapter and Conference Paper
Convolutional Transform Learning
This work proposes a new representation learning technique called convolutional transform learning. In standard transform learning, a dense basis is learned that analyses the image to generate the representati...
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Article
Dual Block-Coordinate Forward-Backward Algorithm with Application to Deconvolution and Deinterlacing of Video Sequences
Optimization methods play a central role in the solution of a wide array of problems encountered in various application fields, such as signal and image processing. Especially when the problems are highly dime...
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Article
A block coordinate variable metric forward–backward algorithm
A number of recent works have emphasized the prominent role played by the Kurdyka-Łojasiewicz inequality for proving the convergence of iterative algorithms solving possibly nonsmooth/nonconvex optimization pr...
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Article
Variable Metric Forward–Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function
We consider the minimization of a function G defined on \({ \mathbb{R} } ^{N}\) , which is the sum of a (not necessarily...
