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Titel
Recovering piecewise smooth multichannel images by minimization of convex functionals with total generalized variation penalty
VerfasserBredies, Kristian In der Gemeinsamen Normdatei der DNB nachschlagen
Erschienen in
Lecture notes in computer science, Berlin [u.a.] : Springer, 1.1973 -, S. 44-77
SpracheEnglisch
DokumenttypAufsatz in einem Sammelwerk
Schlagwörter (EN)Total generalized variation / multichannel images / primal-dual algorithms / image denoising / image deblurring / zooming / dequantization / compressive imaging
ISBN978-3-642-54774-4
ISSN0302-9743
URNurn:nbn:at:at-ubg:3-2098 Persistent Identifier (URN)
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Recovering piecewise smooth multichannel images by minimization of convex functionals with total generalized variation penalty [6.34 mb]
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Zusammenfassung (Englisch)

We study and extend the recently introduced total generalized variation (TGV) functional for multichannel images. This functional has already been established to constitute a well-suited convex model for piecewise smooth scalar images. It comprises exactly the functions of bounded variation but is, unlike purely total-variation based functionals, also aware of higher-order smoothness. For the multichannel version which is developed in this paper, basic properties and existence of minimizers for associated variational problems regularized with second-order TGV is shown. Furthermore, we address the design of numerical solution methods for the minimization of functionals with TGV2 penalty and present, in particular, a class of primal-dual algorithms. Finally, the concrete realization for various image processing problems, such as image denoising, deblurring, zooming, dequantization and compressive imaging, are discussed and numerical experiments are presented.

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