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The class of cartoon-like functions, classicly defined as piecewise $C^2$ functions consisting of smooth regions separated by $C^2$ discontinuity curves, is a well-established model for image data. The quest for optimal approximation of…

Functional Analysis · Mathematics 2016-12-06 Martin Schäfer

In this paper, we present a theoretical analysis of separating images consisting of pointlike and $C^{ \beta}$-curvelike structures, where $\beta \in (1,2] $. Our approach is based on $l_1$-minimization, in which the sparsity of the desired…

Functional Analysis · Mathematics 2021-08-31 Van Tiep Do , Alex Goeßmann

Within the area of applied harmonic analysis, various multiscale systems such as wavelets, ridgelets, curvelets, and shearlets have been introduced and successfully applied. The key property of each of those systems are their (optimal)…

Functional Analysis · Mathematics 2014-07-17 Philipp Grohs , Sandra Keiper , Gitta Kutyniok , Martin Schäfer

Cartoon-like images, i.e., C^2 functions which are smooth apart from a C^2 discontinuity curve, have by now become a standard model for measuring sparse (non-linear) approximation properties of directional representation systems. It was…

Functional Analysis · Mathematics 2015-03-13 G. Kutyniok , W. Lim

The suboptimal performance of wavelets with regard to the approximation of multivariate data gave rise to new representation systems, specifically designed for data with anisotropic features. Some prominent examples of these are given by…

Functional Analysis · Mathematics 2016-01-11 Axel Flinth , Martin Schäfer

Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient…

Functional Analysis · Mathematics 2011-08-08 Gitta Kutyniok , Jakob Lemvig , Wang-Q Lim

Shearlet systems have so far been only considered as a means to analyze $L^2$-functions defined on $\R^2$, which exhibit curvilinear singularities. However, in applications such as image processing or numerical solvers of partial…

Functional Analysis · Mathematics 2010-07-20 Gitta Kutyniok , Wang-Q Lim

We consider the rate of piecewise constant approximation to a locally stationary process $X(t),t\in [0,1]$, having a variable smoothness index $\alpha(t)$. Assuming that $\alpha(\cdot)$ attains its unique minimum at zero and satisfies the…

Probability · Mathematics 2015-11-19 Enkelejd Hashorva , Mikhail Lifshits , Oleg Seleznjev

This paper is concerned with near-optimal approximation of a given function $f \in L_2([0,1])$ with elements of a polynomially enriched wavelet frame, a so-called quarklet frame. Inspired by $hp$-approximation techniques of Binev, we use…

Numerical Analysis · Mathematics 2023-01-11 Stephan Dahlke , Marc Hovemann , Thorsten Raasch , Dorian Vogel

Natural images are typically a composition of cartoon and texture structures. A medical image might, for instance, show a mixture of gray matter and the skull cap. One common task is to separate such an image into two single images, one…

Functional Analysis · Mathematics 2012-04-30 Gitta Kutyniok

BV functions cannot be approximated well by piecewise constant functions, but this work will show that a good approximation is still possible with (countably) piecewise affine functions. In particular, this approximation is area-strictly…

Analysis of PDEs · Mathematics 2015-07-23 Jan Kristensen , Filip Rindler

Shearlet tight frames have been extensively studied during the last years due to their optimal approximation properties of cartoon-like images and their unified treatment of the continuum and digital setting. However, these studies only…

Functional Analysis · Mathematics 2015-03-13 P. Kittipoom , G. Kutyniok , W. Lim

We present CartoonX (Cartoon Explanation), a novel model-agnostic explanation method tailored towards image classifiers and based on the rate-distortion explanation (RDE) framework. Natural images are roughly piece-wise smooth signals --…

Artificial Intelligence · Computer Science 2022-10-21 Stefan Kolek , Duc Anh Nguyen , Ron Levie , Joan Bruna , Gitta Kutyniok

We use deep sparsely connected neural networks to measure the complexity of a function class in $L^2(\mathbb R^d)$ by restricting connectivity and memory requirement for storing the neural networks. We also introduce representation system -…

Machine Learning · Computer Science 2021-08-17 Khay Boon Hong

We study efficient and reliable methods of capturing and sparsely representing anisotropic structures in 3D data. As a model class for multidimensional data with anisotropic features, we introduce generalized three-dimensional cartoon-like…

Functional Analysis · Mathematics 2012-06-05 Gitta Kutyniok , Jakob Lemvig , Wang-Q Lim

We study the necessary and sufficient complexity of ReLU neural networks---in terms of depth and number of weights---which is required for approximating classifier functions in $L^2$. As a model class, we consider the set $\mathcal{E}^\beta…

Functional Analysis · Mathematics 2018-05-23 Philipp Petersen , Felix Voigtlaender

Piecewise constant image approximations of sequential number of segments or clusters of disconnected pixels are treated. The method of majorizing of optimal approximation sequence by hierarchical sequence of image approximations is…

Computer Vision and Pattern Recognition · Computer Science 2013-10-02 M. Kharinov

We demonstrate that shearlet systems yield superior $N$-term approximation rates compared with wavelet systems of functions whose first or higher order derivatives are smooth away from smooth discontinuity curves. We will also provide an…

Functional Analysis · Mathematics 2016-01-13 Philipp Petersen

This work presents several new results concerning the analysis of the convergence of binary, univariate, and linear subdivision schemes, all related to the {\it contractivity factor} of a convergent scheme. First, we prove that a convergent…

Numerical Analysis · Mathematics 2024-05-24 Nira Dyn , Nir Sharon

An algorithm is proposed for the segmentation of image into multiple levels using mean and standard deviation in the wavelet domain. The procedure provides for variable size segmentation with bigger block size around the mean, and having…

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