Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-14582
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Main Title: Asymptomatic Analysis of Inpainting via Universal Shearlet Systems
Author(s): Genzel, Martin
Kutyniok, Gitta
Type: Research Paper
URI: https://depositonce.tu-berlin.de/handle/11303/15809
http://dx.doi.org/10.14279/depositonce-14582
License: http://rightsstatements.org/vocab/InC/1.0/
Abstract: Recently introduced inpainting algorithms using a combination of applied harmonic analysis and compressed sensing have turned out to be very successful. One key ngredient is a carefully chosen representation system which provides (optimally) sparse approximations of the original image. Due to the common assumption that images are typically governed by anisotropic features, directional representation systems have often been utilized. One prominent example of this class are shearlets, which have the additional benefit to allowing faithful implementations. Numerical results show that shearlets significantly outperform wavelets in inpainting tasks. One of those software packages, www.shearlab.org, even offers the flexibility of using a different parameter for each scale, which is not yet covered by shearlet theory. In this paper, we first introduce universal shearlet systems which are associated with an arbitrary scaling sequence, thereby modeling the previously mentioned flexibility. In addition, this novel construction allows for a smooth transition between wavelets and shearlets and therefore enables us to analyze them in a uniform fashion. For a large class of such scaling sequences, we first prove that the associated universal shearlet systems form band-limited Parseval frames for L2(R2) consisting of Schwartz functions. Secondly, we analyze the performance for inpainting of this class of universal shearlet systems within a distributional model situation using an ℓ1-analysis minimization algorithm for reconstruction. Our main result in this part states that, provided the scaling sequence is comparable to the size of the (scale-dependent) gap, nearly-perfect inpainting is achieved at sufficiently fine scales.
Subject(s): co-sparsity
compressed sensing
inpainting
ℓ 1 minimization
multiscale representation systems
shear- lets
sparse approximation
wavelets
Issue Date: 10-Oct-2014
Date Available: 17-Dec-2021
Language Code: en
DDC Class: 510 Mathematik
MSC 2000: 68U10 Image processing
42C40 Wavelets
Series: Preprint-Reihe des Instituts für Mathematik, Technische Universität Berlin
Series Number: 2014, 19
ISSN: 2197-8085
TU Affiliation(s): Fak. 2 Mathematik und Naturwissenschaften » Inst. Mathematik
Appears in Collections:Technische Universität Berlin » Publications

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