Search Results for author:
Found 21 papers, 7 papers with code
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
This paper introduces a new data augmentation architecture that generates synthetic multiparametric (T1 arterial, T1 portal, and T2) magnetic resonance images (MRI) of massive macrotrabecular subtype hepatocellular carcinoma with their corresponding tumor masks through a generative deep learning approach.
Deep Ultrasound Denoising Using Diffusion Probabilistic Models
To improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound.
DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration
This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics.
Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized Solutions
During the past few years, inverse problem formulations of ultrasound beamforming have attracted a growing interest.
A Unifying Approach to Inverse Problems of Ultrasound Beamforming and Deconvolution
In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF.
A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory
Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising.
Image Denoising Inspired by Quantum Many-Body physics
Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising.
Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images
A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics.
Fast High Resolution Blood Flow Estimation and Clutter Rejection via an Alternating Optimization Problem
This paper introduces a computationally efficient technique for estimating high-resolution Doppler blood flow from an ultrafast ultrasound image sequence.
Image and Video Processing
A Novel Fast 3D Single Image Super-Resolution Algorithm
This paper introduces a novel computationally efficient method of solving the 3D single image super-resolution (SR) problem, i. e., reconstruction of a high-resolution volume from its low-resolution counterpart.
Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme
This paper introduces a new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme based on a recently proposed denoiser using the Schroedinger equation's solutions of quantum physics.
Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging
This paper addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images.
Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization
In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients.
Quantum mechanics-based signal and image representation: application to denoising
Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis.
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT
In this article this factorization framework is investigated for single image resolution enhancement with an off-line estimate of the system point spread function.
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation
This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC).
Reconstruction of Enhanced Ultrasound Images From Compressed Measurements Using Simultaneous Direction Method of Multipliers
Through this model, the resolution of reconstructed ultrasound images from compressed measurements mainly depends on three aspects: the acquisition setup, i. e. the incoherence of the sampling matrix, the image regularization, i. e. the sparsity prior, and the optimization technique.
Fast Single Image Super-Resolution
Specifically, an analytical solution can be obtained and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an $\ell_2$-regularized quadratic model, i. e., an $\ell_2$-$\ell_2$ optimization problem.
Beamforming through regularized inverse problems in ultrasound medical imaging
In this paper, we propose to perform beamforming in ultrasound imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered.
Compressive Deconvolution in Medical Ultrasound Imaging
The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams.
Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors
Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution.
