Convergence in variation for the multidimensional generalized sampling series and applications to smoothing for digital image processing
DOI10.5186/aasfm.2020.4532zbMath1458.94180arXiv1906.03021OpenAlexW3034367242MaRDI QIDQ5118925
Laura Angeloni, Gianluca Vinti, Danilo Costarelli
Publication date: 27 August 2020
Published in: Annales Academiae Scientiarum Fennicae Mathematica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1906.03021
convergence in variationsampling-Kantorovich operatorssmoothing in digital image processingmultidimensional generalized sampling seriesvariation diminishing type property
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Interpolation in approximation theory (41A05) Approximation by other special function classes (41A30) Sampling theory in information and communication theory (94A20)
Related Items (10)
Cites Work
- Approximation by max-product neural network operators of Kantorovich type
- Convergence and rate of approximation in \(BV^{\phi}(\mathbb R^N_+)\) for a class of Mellin integral operators
- Scalar multivariate subdivision schemes and box splines
- On approximation properties of generalized Kantorovich-type sampling operators
- Convergence and rate of approximation for linear integral operators in \(BV^{\varphi }\)-spaces in multidimensional setting
- The sampling theorem and linear prediction in signal analysis
- Approximation results with respect to multidimensional \(\varphi \)-variation for nonlinear integral operators
- \(L^p\)-approximation by truncated max-product sampling operators of Kantorovich-type based on Fejér kernel
- Quantitative estimates for sampling type operators with respect to the Jordan variation
- Equivalent definitions of \(BV\) space and of total variation on metric measure spaces
- The conversion matrix between uniform B-spline and Bézier representations
- Lobachevsky spline functions and interpolation to scattered data
- Approximation of discontinuous signals by sampling Kantorovich series
- A characterization of the absolute continuity in terms of convergence in variation for the sampling Kantorovich operators
- Pointwise and uniform approximation by multivariate neural network operators of the max-product type
- Detection of thermal bridges from thermographic images by means of image processing approximation algorithms
- Approximation in variation by homothetic operators in multidimensional setting.
- Max-product neural network and quasi-interpolation operators activated by sigmoidal functions
- Are Natural Images of Bounded Variation?
- An L 1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition
- Total Generalized Variation in Diffusion Tensor Imaging
- A class of spline functions for landmark-based image registration
- A smoothing spline that approximates Laplace transform functions only known on measurements on the real axis
- Convergence in variation and a characterization of the absolute continuity
- A general approach to the convergence theorems of generalized sampling series
- Edge-preserving and scale-dependent properties of total variation regularization
- Approximation results for nonlinear integral operators in modular spaces and applications
- A characterization of the convergence in variation for the generalized sampling series
- Prediction by Samples From the Past With Error Estimates Covering Discontinuous Signals
- Interpolation with variably scaled kernels
- An Inverse Result of Approximation by Sampling Kantorovich Series
- Generalized Sampling Approximation for Multivariate Discontinuous Signals and Applications to Image Processing
- Bounded Variation and Around
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