arXiv subjects

Danilo Costarelli

Publications and source records attributed to Danilo Costarelli.

At least 19 recordsLinked to original sources

arXiv2026

A Kantorovich version of Bernstein-type logarithmic operators

In this paper, we introduce a Kantorovich version of the Bernstein-type logarithmic operators. The idea comes from the wide literature concerning exponential polynomials that preserve exponential functions: here, the exponential weights are replaced by logarithmic ones and the corresponding operators preserve the logarithmic functions. The pointwise, the uniform and the $L^p$ convergence are first established. Then, a Voronovskaja-type asymptotic formula is derived: from it, a second-order differential operator naturally arises, allowing the characterization of the corresponding saturation class. Finally, quantitative estimates for the order of approximation are provided in the continuous case, in terms of the modulus of continuity, and, in the $L^p$ case, by means of suitable $K$-functionals.

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arXiv2026

A new class of positive linear operators preserving logarithmic functions

In this paper, we introduce a new class of positive linear operators that generalize the classical Bernstein operators. Specifically, we construct a sequence of operators that reproduce the logarithmic function $\ln(1+\mu+x)$, with $\mu > 0$ and $x \in [0,1]$. We prove pointwise and uniform convergence and we derive a quantitative estimate of the approximation error in terms of the modulus of continuity. We also obtain a Voronovskaja-type asymptotic formula, that is used to establish saturation results and inverse theorems. In particular, the saturation class of the considered approximation process is characterized by solving a second order differential equation. Shape-preserving properties, such as monotonicity, concavity and variation diminishing, are also investigated. Finally, a simple application to signal denoising is addressed.

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arXiv2025

Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising

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 posterior distribution. The peculiar feature of PCA is their intrinsic parallel nature, which allows for a straightforward parallel implementation that allows the exploitation of parallel computing architecture in a natural and efficient manner. We compare the performance of the PCA method with the standard Gibbs sampler on an image denoising task in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The numerical results and the large speedups obtained with this approach suggest that PCA-based algorithms are a promising alternative for Bayesian inference in high-dimensional inverse problems.

stat.CO↗

arXiv2025

Semi-discrete moduli of smoothness and their applications in one- and two- sided error estimates

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 J. Approx. Theory), and we establish very general one- and two- sided error estimates under non-restrictive assumptions for pointwise linear operators. The proposed results have been proved exploiting the regularization and approximation properties of certain Steklov integrals introduced by Sendov and Popov in 1983. By the definition of semi-discrete moduli of smoothness here proposed, we derive sharper estimates than those that can be achieved by the classical averaged moduli of smoothness ($\tau$-moduli). Furthermore, a Rathore-type theorem is established, and a new notion of K-functional is also introduced showing its equivalence with the semi-discrete modulus of smoothness and its realization. One-sided estimates of approximation can be established for classical operators on bounded domains, such as the Bernstein polynomials. In the case of approximation operators on the whole real line, one-sided estimates can be achieved, e.g., for the Shannon sampling (cardinal) series, as well as for the so-called generalized sampling operators.

math.NA↗

arXiv2025

Saturation theorems for neural network operators by solving elliptic and hyperbolic PDEs with analytical and semi-analytical inverse problems

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 analytical and semi-analytical inverse theorems. One of the key aspects in addressing these issues is solving of certain elliptic and hyperbolic partial differential equations (PDEs), as well as suitable asymptotic formulas for the NN operators based on sufficiently smooth functions; the connection between these two topics lies in the application of the so-called generalized parabola technique by Ditzian. From the saturation theorems characterizations of the saturation classes are derived; these are respectively related to harmonic functions and to the solution of a certain transport equation. Analytical inverse theorems, on the other hand, are related to sub-harmonic functions as well as to functions in the Sobolev space $W^2_\infty$. Finally, the problem of reconstructing data affected by noise is addressed, along with a semi-analytical inverse problem. The latter serves as the starting point for deriving a retrieval procedure that may be useful in real world applications.

math.FA↗

arXiv2025

Sampling Kantorovich operators for speckle noise reduction and gap filling with some applications to remote sensing

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 reduction. To understand the accuracy performances of the proposed algorithms, we first derive a quantitative estimate in $C(\R^n)$ for the error of approximation using the Euler-Maclaurin summation formula, under weak regularity conditions. We also establish a convergence result and a quantitative estimate with respect to the dissimilarity index measured by the continuous SSIM for functions in Lebesgue spaces. Additionally, we prove a multidimensional linear prediction result, which is used to design a new SK-based reconstruction algorithm to handle missing data, that we call LP-SK algorithm. To address speckle noise, we integrate SK operators into a newly proposed Down-Up scaling approach. Numerical tests are presented on synthetic and real SAR images to validate the proposed methods. Performance is assessed using similarity metrics such as SSIM and PSNR, along with speckle-specific indexes. Comparative analysis with state-of-the-art techniques highlights the effectiveness of the proposed approaches.

math.NA↗

arXiv2025

Modular convergence of Steklov sampling operators in Orlicz spaces

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 established following a density approach. To do this, a Luxemburg norm convergence for the Steklov sampling series based on continuous functions with compact support, and a modular-type inequality in the case of functions in Orlicz spaces has been preliminary proved. As a particular case of general theory, the results in $L^p$, in the Zygmund (interpolation), and in the exponential spaces are deduced. A crucial aspect in the above results is the choice of both band- and duration- limited kernel functions satisfying the partition of the unit property; to provide such examples an equivalent condition based on the Poisson summation formula and the computation of the Fourier transform of the kernel has been employed.

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arXiv2025

A characterization of generalized Lipschitz classes by the rate of convergence of semi-discrete operators

In this paper, we establish a comprehensive characterization of the generalized Lipschitz classes through the study of the rate of convergence of a family of semi-discrete sampling operators, of Durrmeyer type, in $L^p$-setting. To achieve this goal, we provide direct approximation results, which lead to quantitative estimates based on suitable $K$-functionals in Sobolev spaces and, consequently, on higher-order moduli of smoothness. Additionally, we introduce a further approach employing the celebrated Hardy-Littlewood maximal inequality to weaken the assumptions required on the kernel functions. These direct theorems are essential for obtaining qualitative approximation results in suitable Lipschitz and generalized Lipschitz classes, as they also provide conditions for studying the rate of convergence when functions belonging to Sobolev spaces are considered. The converse implication is, in general, delicate, and actually consists in addressing an inverse approximation problem allowing to deduce regularity properties of a function from a given rate of convergence. Thus, through both direct and inverse results, we establish the desired characterization of the considered Lipschitz classes based on the $L^p$-convergence rate of Durrmeyer sampling operators. Finally, we provide remarkable applications of the theory, based on suitable combinations of kernels that satisfy the crucial Strang-Fix type condition used here allowing to both enhance the rate of convergence and to predict the signals.

math.FA↗

arXiv2025

Image resizing by neural network operators and their convergence rate with respect to the $L^p$-norm and the dissimilarity index defined through the continuous SSIM

In literature, several algorithms for imaging based on interpolation or approximation methods are available. The implementation of theoretical processes highlighted the necessity of providing theoretical frameworks for the convergence and error estimate analysis to support the experimental setups. In this paper, we establish new techniques for deriving quantitative estimates for the order of approximation for multivariate linear operators of the pointwise-type, with respect to the $L^p$-norm and to the so-called dissimilarity index defined through the continuous SSIM. In particular, we consider a family of approximation operators known as neural network (NN) operators, that have been widely studied in the last years in view of their connection with the theory of artificial neural networks. For these operators, we first establish sharp estimates in case of $C^1$ and piecewise (everywhere defined) $C^1$-functions. Then, the case of functions modeling digital images is considered, and specific quantitative estimates are achieved, including those with respect to the mentioned dissimilarity index. Moreover, the above analysis has also been extended to $L^p$-spaces, using a new constructive technique, in which the multivariate averaged modulus of smoothness has been employed. Finally, numerical experiments of image resizing have been given to support the theoretical results. The accuracy of the proposed algorithm has been evaluated through similarity indexes such as SSIM, likelihood index (S-index) and PSNR, and compared with other rescaling methods, including bilinear, bicubic, and upscaling-de la Vall\'ee-Poussin interpolation (u-VPI). Numerical simulations show the effectiveness of the proposed method for image processing tasks, particularly in terms of the aforementioned SSIM, and are consistent with the provided theoretical analysis.

math.NA↗

arXiv2025

Strong and weak sharp bounds for Neural Network Operators in Sobolev-Orlicz spaces and their quantitative extensions to Orlicz spaces

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 both strong (in terms of the Luxemburg norm) and weak (in terms of the modular functional) estimates, using different approaches. The strong estimates are derived for spaces generated by $\varphi$-functions that are $N$-functions or satisfy the $\Delta^\prime$-condition. Such estimates also lead to convergence results with respect to the Luxemburg norm in several instances of Orlicz spaces, including the exponential case. Meanwhile, the weak estimates are achieved under less restrictive assumptions on the involved $\varphi$-function. To obtain these results, we introduce some new tools and techniques in Orlicz spaces. Central to our approach is the Orlicz Minkowski inequality, which allows us to obtain unified strong estimates for the operators. We also present a weak (modular) version of this inequality holding under weaker conditions. Additionally, we introduce a novel notion of discrete absolute $\varphi$-moments of the hybrid type, and we employ the Hardy-Littlewood maximal operator within Orlicz spaces for the asymptotic analysis. Furthermore, we introduce the new space $\mathcal{W}^{1,\varphi}(I)$, which is embedded in the Sobolev-Orlicz space $W^{1,\varphi}(I)$ and modularly dense in $L^\varphi(I)$. This allows to achieve asymptotic estimates for a wider class of $\varphi$-functions, including those that do not meet the $\Delta_2$-condition. For the extension to the whole Orlicz-setting, we generalize a Sobolev-Orlicz density result given by H. Musielak using Steklov functions, providing a modular counterpart.

math.FA↗

arXiv2024

Microwave Remote Sensing of Soil Moisture, Above Ground Biomass and Freeze-Thaw Dynamic: Modeling and Empirical Approaches

Human actions have accelerated changes in global temperature, precipitation patterns, and other critical Earth systems. Key markers of these changes can be linked to the dynamic of Essential Climate Variables (ECVs) and related quantities, such as Soil Moisture (SM), Above Ground Biomass (AGB), and Freeze-Thaw (FT) Dynamics. These variables are crucial for understanding global climate changes, hydrological and carbon cycles included. Monitoring these variables helps to validate climate models and inform policy decisions. Technologies like microwave remote sensing provide critical tools for monitoring the effects of human activities on these variables at a global scale. Other than proper tachenological developments, the study of ECVs requires suitable theoretical retrieval tools, which leads to the solutions of inverse problems. In this brief survey, we analyze and summarize the main retrieval techniques available in the literature for SM, AGB, and FT, performed on data collected with microwave remote sensing sensors. Such methods will be some of the fundamental algorithms that can find applications in the research activities of the interdisciplinary, curiosity-driven, project {\it REmote sensing daTa INversion with multivariate functional modeling for essential climAte variables characterization (RETINA)}, recently funded by the European Union under the Italian National Recovery and Resilience Plan of NextGenerationEU, under the Italian Ministry of University and Research. The main goal of RETINA, in which three research units from three different italian universities are involved, is to create innovative techniques for analyzing data generated by the interaction of electromagnetic waves with the Earth's surface, applying theoretical insights to address real-world challenges.

math.NA↗

arXiv2024

Implementation of neural network operators with applications to remote sensing data

In this paper, we provide two algorithms based on the theory of multidimensional neural network (NN) operators activated by hyperbolic tangent sigmoidal functions. Theoretical results are recalled to justify the performance of the here implemented algorithms. Specifically, the first algorithm models multidimensional signals (such as digital images), while the second one addresses the problem of rescaling and enhancement of the considered data. We discuss several applications of the NN-based algorithms for modeling and rescaling/enhancement remote sensing data (represented as images), with numerical experiments conducted on a selection of remote sensing (RS) images from the (open access) RETINA dataset. A comparison with classical interpolation methods, such as bilinear and bicubic interpolation, shows that the proposed algorithms outperform the others, particularly in terms of the Structural Similarity Index (SSIM).

math.NA↗

arXiv2024

Advancements in nonlinear exponential sampling: convergence, quantitative analysis and Voronovskaya-type formula

In this paper, we introduce the nonlinear exponential Kantorovich sampling series. We establish pointwise and uniform convergence properties and a nonlinear asymptotic formula of the Voronovskaja-type given in terms of the limsup. Furthermore, we extend these convergence results to Mellin-Orlicz spaces with respect to the logarithmic (Haar) measure. Quantitative results are also given, using the log-modulus of continuity and the log-modulus of smoothness, respectively, for log-uniformly continuous functions and for functions in Mellin-Orlicz spaces. Consequently, the qualitative order of convergence can be obtained in case of functions belonging to suitable Lipschitz (log-H\"olderian) classes.

math.FA↗

arXiv2024

Simultaneous approximation by neural network operators with applications to Voronovskaja formulas

In this paper, we considered the problem of the simultaneous approximation of a function and its derivatives by means of the well-known neural network (NN) operators activated by sigmoidal function. Other than a uniform convergence theorem for the derivatives of NN operators, we also provide a quantitative estimate for the order of approximation based on the modulus of continuity of the approximated derivative. Furthermore, a qualitative and quantitative Voronovskaja-type formula is established, which provides information about the high order of approximation that can be achieved by NN operators. To prove the above theorems, several auxiliary results involving sigmoidal functions have been established. At the end of the paper, noteworthy examples have been discussed in detail.

math.FA↗

arXiv2024

Approximation processes by multidimensional Bernstein-type exponential polynomials on the hypercube

In this paper we introduce a new family of Bernstein-type exponential polynomials on the hypercube $[0, 1]^d$ and study their approximation properties. Such operators fix a multidimensional version of the exponential function and its square. In particular, we prove uniform convergence, by means of two different approaches, as well as a quantitative estimate of the order of approximation in terms of the modulus of continuity of the approximated function.

math.CA↗

arXiv2023

A comparison among a fuzzy algorithm for image rescaling with other methods of digital image processing

The aim of this paper is to present a comparison among the fuzzy-type algorithm for image rescaling introduced by Jurio et al., 2011, quoted in the list of references, with some other existing algorithms such as the classical bicubic algorithm and the so-called sampling Kantorovich (SK) one. Note that, the SK algorithm is a recent tool for image rescaling and enhancement that revealed to be useful in several applications to real world problems, while bicubic algorithm is widely known in the literature. The comparison among the above mentioned algorithms (all implemented by MatLab programming language) has been done in term of suitable similarity indexes such as the Peak-Signal-to-Noise-Ratio (PSNR) and the likelihood index $S$. Moreover, also the CPU time of the considered algorithms has been analysed.

math.FA↗

arXiv2023

Max-product Kantorovich sampling operators: quantitative estimates in functional spaces

In this paper, we study the order of approximation for max-product Kantorovich sampling operators based upon generalized kernels in the setting of Orlicz spaces. We establish a quantitative estimate for the considered family of sampling-type operators using the Orlicz-type modulus of smoothness, which involves the modular functional of the space. From this result, it is possible to obtain the qualitative order of convergence when functions belonging to suitable Lipschitz classes are considered. On the other hand, in the compact case, we exploit a suitable definition of K-functional in Orlicz spaces in order to provide an upper bound for the approximation error of the involved operators. The treatment in the general framework of Orlicz spaces allows one to obtain a unifying theory on the rate of convergence, as the proved results can be deduced for a wide range of functional spaces, such as $L^{p}$-spaces, interpolation spaces and exponential spaces.

math.FA↗

arXiv2023

Convergence results in Orlicz spaces for sequences of max-product Kantorovich sampling operators

In this paper, we provide a unifying theory concerning the convergence properties of the so-called max-product Kantorovich sampling operators based upon generalized kernels in the setting of Orlicz spaces. The approximation of functions defined on both bounded intervals and on the whole real axis has been considered. Here, under suitable assumptions on the kernels, considered in order to define the operators, we are able to establish a modular convergence theorem for these sampling-type operators. As a direct consequence of the main theorem of this paper, we obtain that the involved operators can be successfully used for approximation processes in a wide variety of functional spaces, including the well-known interpolation and exponential spaces. This makes the Kantorovich variant of max-product sampling operators suitable for reconstructing not necessarily continuous functions (signals) belonging to a wide range of functional spaces. Finally, several examples of Orlicz spaces and of kernels for which the above theory can be applied are presented.

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