61 citations · 67 across the 8 of their papers we have counts for
9 papers
Beyond Convolution: A Taxonomy of Structured Operators for Learning-Based Image Processing
Simone Cammarasana
The convolution operator is the fundamental building block of modern convolutional neural networks (CNNs), owing to its simplicity, translational equivariance, and efficient implem…
Framework of a multiscale data-driven DT of the musculoskeletal system
Martina Paccini, Simone Cammarasana, Giuseppe Patanè
Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, requiring advanced diagnostic and therapeutic tools for personalised assessment and treatment. Effecti…
Optimal Weighted Convolution for Classification and Denosing
Simone Cammarasana, Giuseppe Patanè
We introduce a novel weighted convolution operator that enhances traditional convolutional neural networks (CNNs) by integrating a spatial density function into the convolution ope…
Optimal Density Functions for Weighted Convolution in Learning Models
Simone Cammarasana, Giuseppe Patanè
The paper introduces the weighted convolution, a novel approach to the convolution for signals defined on regular grids (e.g., 2D images) through the application of an optimal dens…
Learning-Based and Quality Preserving Super-Resolution of Noisy Images
Simone Cammarasana, Giuseppe Patanè
Several applications require the super-resolution of noisy images and the preservation of geometrical and texture features. State-of-the-art super-resolution methods do not account…
HPC-based Solvers of Minimisation Problems for Signal Processing
Simone Cammarasana, Giuseppe Patanè
Several physics and engineering applications involve the solution of a minimisation problem to compute an approximation of the input signal. Modern computing hardware and software…
Learning-based Framework for US Signals Super-resolution
Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè
We propose a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. We up-sample the acquired low-r…
Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs
Antonella Galizia, Simone Cammarasana, Andrea Clematis +1
Partial Differential Equations (PDEs) describe several problems relevant to many fields of applied sciences, and their discrete counterparts typically involve the solution of spars…
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images
Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè
Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodol…