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Harmonic analysis of the space BV.

TLDR
In this paper, it was shown that if a function f is in BV, its coefficient sequence in a normalized wavelet basis satisfies a class of weak-� 1 type estimates.
Abstract
We establish new results on the space BV of functions with bounded variation. While it is well known that this space admits no unconditional basis, we show that it is “almost” characterized by wavelet expansions in the following sense: if a function f is in BV, its coefficient sequence in a BV normalized wavelet basis satisfies a class of weak-� 1 type estimates. These weak estimates can be employed to prove many interesting results. We use them to identify the interpolation spaces between BV and Sobolev or Besov spaces, and to derive new Gagliardo-Nirenberg-type inequalities. 1. Background and main results

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Citations
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Journal ArticleDOI

On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs

TL;DR: It is proved that Haar wavelet shrinkage on a single scale is equivalent to a single step of space-discrete TV diffusion or regularization of two-pixel pairs, and it is shown that waveletshrinkage on multiple scales can be regarded as asingle step diffusion filtering orregularization of the Laplacian pyramid of the signal.
ReportDOI

Variational PDE models in image processing

TL;DR: A broad picture of mathematical image processing is given through one of the most recent and very successful approaches - the variational PDE method, which discusses two crucial ingredients for image processing: image modeling or representation, and processor modeling.
Journal ArticleDOI

A multiscale image representation using hierarchical (bv, l 2 ) decompositions ∗

TL;DR: A new multiscale image decomposition which offers a hierarchical, adaptive representation for the different features in general images is proposed and the questions of convergence, energy decomposition, localization, and adaptivity are discussed.
Journal ArticleDOI

Iterative Regularization and Nonlinear Inverse Scale Space Applied to Wavelet-Based Denoising

TL;DR: This paper generalizes the iterative regularization method and the inverse scale space method, recently developed for total-variation (TV) based image restoration, to wavelet-based image Restoration, obtaining significant improvement over the Rudin-Osher-Fatemi TV-based restoration.
References
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Book

Ten lectures on wavelets

TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI

Ten Lectures on Wavelets

TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Book

Measure theory and fine properties of functions

TL;DR: In this article, the authors define and define elementary properties of BV functions, including the following: Sobolev Inequalities Compactness Capacity Quasicontinuity Precise Representations of Soboleve Functions Differentiability on Lines BV Function Differentiability and Structure Theorem Approximation and Compactness Traces Extensions Coarea Formula for BV Functions isoperimetric inequalities The Reduced Boundary The Measure Theoretic Boundary Gauss-Green Theorem Pointwise Properties this article.

Ondelettes et operateurs I

Yves Meyer
TL;DR: In this paper, the authors propose a new theory of Fourier, which tend to remplacer dans certains cases l'analyse de Fourier and tend to re-applique a divers secteurs de la science and de la technologie comme la turbulence, le traitement du signal, l'acoustique musicale, etc.
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