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Nadir Maaroufi

Publications and source records attributed to Nadir Maaroufi.

7 recordsLinked to original sources
arXiv2026

Vector-Valued Wavelet Bases as Hilbert $\mathbb{M}_m(\mathbb{R})$-Module Bases: A Construction from Scalar Wavelets

Vector-valued multiscale representations are essential when signals or fields take values in $\mathbb{R}^m$ and component interactions carry meaningful information. Most multiwavelet and super-wavelet constructions are formulated in scalar Hilbert-space settings and typically produce channelwise scalar coefficients followed by recombination. We develop an intrinsic framework for vector-valued wavelets on $L^2(\mathbb{R}^d,\mathbb{R}^m)$ by endowing this space with a natural $\mathbb{M}_m(\mathbb{R})$-valued inner product, thereby turning it into a Hilbert $\mathbb{M}_m(\mathbb{R})$-module. This module viewpoint yields matrix-valued coefficients that encode cross-component interactions and provides canonical reconstruction through a Parseval-type identity. Within this setting, we introduce a constructive lifting procedure that builds separable multivariate vector-valued wavelet bases in $L^2(\mathbb{R}^d,\mathbb{R}^m)$ from scalar wavelet bases while preserving compact support, vanishing moments, and regularity.

math.FA↗
arXiv2026

Point-dimension theory (part II): The point-cross dimension

We introduce the Point-Cross Dimension, a new pointwise invariant designed to measure the directional organization of a set at a single point. Whereas the Point-Extended Box Dimension quantifies local dispersion and covering complexity, the Point-Cross Dimension isolates a complementary layer: the coexistence of independent effective directions through the same germ. The construction assigns weights to admissible directional probes and aggregates them over projectively independent channels, thereby turning the elementary intuition of a cross into a flexible local dimension theory. This viewpoint separates phenomena that classical isotropic dimensions often collapse. A point may have small local box dispersion while carrying several independent directional channels. Conversely, large local covering complexity need not reflect genuine directional independence. We develop the theory in three successive layers. The first is a point-vector dimension, which records exact local directions. The second is a point-tangential dimension, which replaces exact directions by Bouligand effective directions. The third is the Point-Cross Dimension, which weights these effective projective channels by the point-extended box complexity detected along admissible probes. We establish the basic structural properties of these invariants and compute the resulting Point-Cross Dimension on a range of model configurations, including finite crosses, fractal coordinate frames, oscillatory germs, self-similar curves, Sierpiński-type carpets, Cantor dusts, and infinite-rank outlook examples. The final part of the paper establishes comparison principles between the directional and dispersive layers of the theory.

math.MG↗
arXiv2025

A Constructive Approach for Building Wavelet Bases in \( L^2(\mathbb{R}^d, \mathbb{R}^m) \) with Optimal Properties

The main contribution of this paper is a constructive method for building separable multivariate vector-valued wavelet bases in the general framework of \( L^2(\mathbb{R}^d, \mathbb{R}^m) \) for any \( d, m \geq 1 \). While separable wavelet bases in \( L^2(\mathbb{R}^d, \mathbb{R}) \) are well-established and widely applied, the explicit construction of truly vector-valued wavelet bases remains an open problem, even in the simplest case of \( L^2(\mathbb{R}, \mathbb{R}^2) \), let alone in \( L^2(\mathbb{R}^2, \mathbb{R}^2) \). In practice, the conventional approach applies standard separable wavelet bases of \( L^2(\mathbb{R}^2, \mathbb{R}) \) independently to each component of vector-valued signals in \( L^2(\mathbb{R}^2, \mathbb{R}^2) \). However, this approach fails to capture the intrinsic vectorial structure of the signals. To address this limitation, we propose a constructive approach within the vector-valued wavelet framework, providing a systematic method for constructing such bases in the general case of \( L^2(\mathbb{R}^d, \mathbb{R}^m) \). By linking \( m \)-multiwavelets to vector-valued wavelets, our approach not only enables the systematic construction of separable multivariate bases in \( L^2(\mathbb{R}^d, \mathbb{R}^m) \) that satisfy the vector-valued multiresolution analysis but also ensures that these bases inherit key structural properties, making them well-suited for practical applications.

math.FA↗
arXiv2024

HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting

Inpainting, for filling missing image regions, is a crucial task in various applications, such as medical imaging and remote sensing. Trending data-driven approaches efficiency, for image inpainting, often requires extensive data preprocessing. In this sense, there is still a need for model-driven approaches in case of application constrained with data availability and quality, especially for those related for time series forecasting using image inpainting techniques. This paper proposes an improved modeldriven approach relying on patch-based techniques. Our approach deviates from the standard Sum of Squared Differences (SSD) similarity measure by introducing a Hybrid Similarity (HySim), which combines both strengths of Chebychev and Minkowski distances. This hybridization enhances patch selection, leading to high-quality inpainting results with reduced mismatch errors. Experimental results proved the effectiveness of our approach against other model-driven techniques, such as diffusion or patch-based approaches, showcasing its effectiveness in achieving visually pleasing restorations.

cs.CV↗
arXiv2022

Point-Dimension Theory (Part I): The Point-Extended Box Dimension

This article is an introductory work to a larger research project devoted to pure, applied and philosophical aspects of dimension theory. It concerns a novel approach toward an alternate dimension theory foundation: the point-dimension theory. For this purpose, historical research on this notion and related concepts, combined with critical analysis and philosophical development proved necessary. Hence, our main objective is to challenge the conventional zero dimension assigned to the point. This reconsideration allows us to propose two new ways of conceiving the notion of dimension, which are the two sides of the same coin. First as an organization; accordingly, we suggest the existence of the Dimensionad, an elementary particle conferring dimension to objects and space-time. The idea of the existence of this particle could possibly adopted as a projection to create an alternative way to unify quantum mechanics and Einstein's general relativity. Secondly, in connection with Boltzmann and Shannon entropies, dimension appears essentially as a comparison between entropies of sets. Thus, we started from the point and succeeded in constructing a point-dimension notion allowing us to extend the principle of box dimension in many directions. More precisely, we introduce the notion of point-extended box dimension in the large framework of topological vector spaces, freeing it from the notion of metric. This general setting permits us to treat the case of finite, infinite and invisible dimensions. This first part of our research project focuses essentially on general properties and is particularly oriented towards establishing a well founded framework for infinite dimension. Among others, one prospect is to test the possibility of using other types of spaces as a setting for quantum mechanics, instead of limiting it to the exclusive Hilbertian framework.

physics.hist-ph↗
arXiv2020

Predicting the Future is like Completing a Painting!

This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images- and signal-based processing procedures. After transforming a time series data into its corresponding image, the problem of data forecasting becomes essentially a problem of image inpainting problem, i.e., completing missing data in the image. An extensive experimental evaluation is conducted using a large dataset proposed by the well-known M3-competition. Results show that FM2I represents an efficient and robust tool for time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 forecasting methods.

cs.AI↗