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Found 8 papers, 0 papers with code
Learning-Based and Quality Preserving Super-Resolution of Noisy Images
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.
US \& MRI Image Fusion Based on Markerless Skin Registration
This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound (US) acquisition.
3D Patient-specific Modelling and Characterisation of Muscle-Skeletal Districts
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.
Learning-based Framework for US Signals Super-resolution
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).
Secure Routine: A Routine-Based Algorithm for Drivers Identification
The introduction of Information and Communication Technology (ICT) in transportation systems leads to several advantages (efficiency of transport, mobility, traffic management).
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images
We define a novel deep learning framework for the real-time denoising of ultrasound images.
Fourier-based and Rational Graph Filters for Spectral Processing
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).
SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning
We formalize this problem as a domain adaptation task and introduce a novel dataset of urban scenes with the related semantic labels.
