3 citations · 7 across the 12 of their papers we have counts for
7 papers · 1 filter
Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising
Danilo Costarelli, Michele Piconi, Alessio Troiani
We propose using Probabilistic Cellular Automata (PCA) to address inverse problems with the Bayesian approach. In particular, we use PCA to sample from an approximation of the post…
Semi-discrete moduli of smoothness and their applications in one- and two- sided error estimates
Danilo Costarelli, Donato Lavella
In this paper, we introduce a new semi-discrete modulus of smoothness, which generalizes the definition given by Kolomoitsev and Lomako (KL) in 2023 (in the paper published in the…
Saturation theorems for neural network operators by solving elliptic and hyperbolic PDEs with analytical and semi-analytical inverse problems
Danilo Costarelli
This paper addresses inverse problems (in a broad sense) for two classes of multivariate neural network (NN) operators, with particular emphasis on saturation results, and both ana…
Modular convergence of Steklov sampling operators in Orlicz spaces
Danilo Costarelli, Erika Russo
In this paper, we deal with the family of Steklov sampling operators in the general setting of Orlicz spaces. The main result of the paper is a modular convergence theorem establis…
Sampling Kantorovich operators for speckle noise reduction and gap filling with some applications to remote sensing
Danilo Costarelli, Mariarosaria Natale
In this paper, we investigate the application of multivariate sampling Kantorovich (SK) operators for image reconstruction, with a particular focus on gap filling and speckle noise…
Image resizing by neural network operators and their convergence rate with respect to the -norm and the dissimilarity index defined through the continuous SSIM
Danilo Costarelli, Mariarosaria Natale, Michele Piconi
In literature, several algorithms for imaging based on interpolation or approximation methods are available. The implementation of theoretical processes highlighted the necessity o…
Strong and weak sharp bounds for Neural Network Operators in Sobolev-Orlicz spaces and their quantitative extensions to Orlicz spaces
Danilo Costarelli, Michele Piconi
In this paper, we establish sharp bounds for a family of Kantorovich-type neural network operators within the general frameworks of Sobolev-Orlicz and Orlicz spaces. We establish b…