Search Results for author:
Found 6 papers, 2 papers with code
Quantum Inspired Approach for Medical Imaging: Applications to Background Noise
Background noise in medical imaging poses significant challenges for accurate diagnosis, prompting the development of denoising algorithms.
Automatic Tuning of Denoising Algorithms Parameters Without Ground Truth
Denoising is omnipresent in image processing.
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.
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.
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.
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.
