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Second order differences of cyclic data and applications in variational denoising.
SIAM J. Imaging Sci. 7, 2916-2953 (2014)
In many image and signal processing applications, such as interferometric synthetic aperture radar (SAR), electroencephalogram (EEG) data analysis, ground-based astronomy, and color image restoration, in HSV or LCh spaces the data has its range on the one-dimensional sphere S-1. Although the minimization of total variation (TV) regularized functionals is among the most popular methods for edge-preserving image restoration, such methods were only very recently applied to cyclic structures. However, as for Euclidean data, TV regularized variational methods suffer from the so-called staircasing effect. This effect can be avoided by involving higher order derivatives into the functional. This is the first paper which uses higher order differences of cyclic data in regularization terms of energy functionals for image restoration. We introduce absolute higher order differences for S-1-valued data in a sound way which is independent of the chosen representation system on the circle. Our absolute cyclic first order difference is just the geodesic distance between points. Similar to the geodesic distances, the absolute cyclic second order differences have only values in [0, pi]. We update the cyclic variational TV approach by our new cyclic second order differences. To minimize the corresponding functional we apply a cyclic proximal point method which was recently successfully proposed for Hadamard manifolds. Choosing appropriate cycles this algorithm can be implemented in an efficient way. The main steps require the evaluation of proximal mappings of our cyclic differences for which we provide analytical expressions. Under certain conditions we prove the convergence of our algorithm. Various numerical examples with artificial as well as real-world data demonstrate the advantageous performance of our algorithm.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Phase-valued Data ; Manifold-valued Data ; Higher Order Differences ; Variational Restoration Methods ; Sar Imaging ; Proximal Mapping; Proximal Point Algorithm; Total Variation Minimization; Total Generalized Variation; Wave-Front Reconstruction; Riemannian-Manifolds; Bounded Variation; Radar Interferometry; Image-Restoration; Earths Surface; Spaces
ISSN (print) / ISBN 1936-4954
Zeitschrift SIAM Journal on Imaging Sciences
Quellenangaben Band: 7, Heft: 4, Seiten: 2916-2953
Verlagsort Philadelphia, Pa.
Institut(e) Institute of Computational Biology (ICB)