56 citations · 184 across the 12 of their papers we have counts for
13 papers
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Karen Sanchez, Carlos Hinojosa, Kevin Arias +4
Data augmentation is classically used to improve the overall performance of deep learning models. It is, however, challenging in the case of medical applications, and in particular…
Single Image Super-Resolution of Noisy 3D Dental CT Images Using Tucker Decomposition
J. Hatvani, A. Basarab, J. Michetti +2
Tensor decomposition has proven to be a strong tool in various 3D image processing tasks such as denoising and super-resolution. In this context, we recently proposed a canonical p…
Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging
Duong-Hung Pham, Adrian Basarab, Ilyess Zemmoura +2
This paper addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. Formulating the separation of clutter and blood c…
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series
Florian Mouret, Mohanad Albughdadi, Sylvie Duthoit +3
This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on…
Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images -- Extended Version
Corbineau Marie-Caroline, Kouamé Denis, Chouzenoux Emilie +2
Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov c…
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT
Janka Hatvani, Adrian Basarab, Jean-Yves Tourneret +2
Available super-resolution techniques for 3D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large…
Adaptive transform via quantum signal processing: application to signal and image denoising
Raphaël Smith, Adrian Basarab, Bertrand Georgeot +1
The main scope of this paper is to show how tools from quantum mechanics, in particular the Schroedinger equation, can be used to construct an adaptive transform suitable for signa…
An axially-variant kernel imaging model applied to ultrasound image reconstruction
Mihai I. Florea, Adrian Basarab, Denis Kouamé +1
Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circ…
Reconstruction of Enhanced Ultrasound Images From Compressed Measurements Using Simultaneous Direction Method of Multipliers
Zhouye Chen, Adrian Basarab, Denis Kouamé
High resolution ultrasound image reconstruction from a reduced number of measurements is of great interest in ultrasound imaging, since it could enhance both the frame rate and ima…
Fast Single Image Super-Resolution
Ningning Zhao, Qi Wei, Adrian Basarab +3
This paper addresses the problem of single image super-resolution (SR), which consists of recovering a high resolution image from its blurred, decimated and noisy version. The exis…
Beamforming through regularized inverse problems in ultrasound medical imaging
Teodora Szasz, Adrian Basarab, Denis Kouamé
Beamforming in ultrasound imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast…
Compressive Deconvolution in Medical Ultrasound Imaging
Zhouye Chen, Adrian Basarab, Denis Kouamé
The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has bee…
Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors
Ningning Zhao, Adrian Basarab, Denis Kouame +1
This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy cha…