no code implementations • 3 Nov 2023 • Simone Cammarasana, Giuseppe Patanè
The experimental results show that our method outperforms learning-based methods, has comparable results with standard methods, preserves the properties of the input image as contours, brightness, and textures, and reduces the artefacts.
no code implementations • 26 Jul 2023 • Martina Paccini, Giacomo Paschina, Stefano De Beni, Andrei Stefanov, Velizar Kolev, Giuseppe Patanè
This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound (US) acquisition.
no code implementations • 18 Apr 2023 • Martina Paccini, Giuseppe Patanè, Michela Spagnuolo
This work addresses the patient-specific characterisation of the morphology and pathologies of muscle-skeletal districts (e. g., wrist, spine) to support diagnostic activities and follow-up exams through the integration of morphological and tissue information.
no code implementations • 17 Apr 2023 • Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè
We qualitatively and quantitatively test our model on different anatomical districts (e. g., cardiac, obstetric) images and with different up-sampling resolutions (i. e., 2X, 4X).
no code implementations • 12 Dec 2021 • Davide Micale, Gianpiero Costantino, Ilaria Matteucci, Giuseppe Patanè, Giampaolo Bella
The introduction of Information and Communication Technology (ICT) in transportation systems leads to several advantages (efficiency of transport, mobility, traffic management).
no code implementations • 22 Jan 2021 • Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè
We define a novel deep learning framework for the real-time denoising of ultrasound images.
no code implementations • 8 Nov 2020 • Giuseppe Patanè
Data are represented as graphs in a wide range of applications, such as Computer Vision (e. g., images) and Graphics (e. g., 3D meshes), network analysis (e. g., social networks), and bio-informatics (e. g., molecules).
no code implementations • 18 Jun 2020 • Daniele Di Mauro, Antonino Furnari, Giuseppe Patanè, Sebastiano Battiato, Giovanni Maria Farinella
We formalize this problem as a domain adaptation task and introduce a novel dataset of urban scenes with the related semantic labels.