Campus Event Calendar
Event Entry
What and Who
Interpolation of Matrix-Valued Images
Prof. Dr. Joachim Weickert
Ringvorlesung
AG 1, AG 2, AG 3, AG 4, AG 5
AG Audience
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Date, Time and Location
Thursday, 17 February 2005
13:00
-- Not specified --
45
016
Saarbrücken
Abstract
Many tasks in image processing, computer vision and computer
graphics require to interpolate images in order to estimate
data at locations where no values are specified.
In this talk a common framework for image interpolation
and regularisation is investigated. This framework is based
on partial differential equations (PDEs) and allows
rotationally invariant models. It can be used for a large
range of applications including resampling, scattered data
interpolation and image inpainting. Experiments illustrate
that it outperforms interpolants with radial basis functions,
it allows discontinuity-preserving interpolation with no
additional oscillations, and it respects a maximum-minimum
principle. These ideas are not restricted to scalar- or
vector-valued images, they can also be applied to matrix-valued
data sets arising e.g. from diffusion tensor magnetic resonance
imaging. In this case the interpolated matrix field preserves
the semidefinite positiveness of the initial data.
graphics require to interpolate images in order to estimate
data at locations where no values are specified.
In this talk a common framework for image interpolation
and regularisation is investigated. This framework is based
on partial differential equations (PDEs) and allows
rotationally invariant models. It can be used for a large
range of applications including resampling, scattered data
interpolation and image inpainting. Experiments illustrate
that it outperforms interpolants with radial basis functions,
it allows discontinuity-preserving interpolation with no
additional oscillations, and it respects a maximum-minimum
principle. These ideas are not restricted to scalar- or
vector-valued images, they can also be applied to matrix-valued
data sets arising e.g. from diffusion tensor magnetic resonance
imaging. In this case the interpolated matrix field preserves
the semidefinite positiveness of the initial data.
Contact
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Bahareh Kadkhodazadeh, 02/15/2005 10:45
Bahareh Kadkhodazadeh, 10/21/2004 12:55 -- Created document.
Bahareh Kadkhodazadeh, 10/21/2004 12:55 -- Created document.