Image Sharpening by Flows Based on Triple Well Potentials
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| Titel: | Image Sharpening by Flows Based on Triple Well Potentials |
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| Autoren: | Guy Gilboa, Nir Sochen, Yehoshua Y. Zeevi |
| Weitere Verfasser: | The Pennsylvania State University CiteSeerX Archives |
| Quelle: | http://tiger.technion.ac.il/~gilboa/pub/JMIV_mia02_GSZ.pdf. |
| Publikationsjahr: | 2003 |
| Bestand: | CiteSeerX |
| Schlagwörter: | image filtering, image enhancement, image sharpening, nonlinear diffusion, hyper- diffusion, variational |
| Beschreibung: | Image sharpening in the presence of noise is formulated as a non-convex variational problem. The energy functional incorporates a gradient-dependent potential, a convex fidelity criterion and a high order convex regularizing term. The first term attains local minima at zero and some high gradient magnitude, thus forming a triple well-shaped potential (in the one-dimensional case). The energy minimization flow results in sharpening of the dominant edges, while most noisy fluctuations are filtered out. |
| Publikationsart: | text |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5447; http://tiger.technion.ac.il/~gilboa/pub/JMIV_mia02_GSZ.pdf |
| Verfügbarkeit: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5447 http://tiger.technion.ac.il/~gilboa/pub/JMIV_mia02_GSZ.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Dokumentencode: | edsbas.D9B6921B |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Image sharpening in the presence of noise is formulated as a non-convex variational problem. The energy functional incorporates a gradient-dependent potential, a convex fidelity criterion and a high order convex regularizing term. The first term attains local minima at zero and some high gradient magnitude, thus forming a triple well-shaped potential (in the one-dimensional case). The energy minimization flow results in sharpening of the dominant edges, while most noisy fluctuations are filtered out. |
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