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Proceedings Paper

Image restoration-based template matching with application to motion estimation
Author(s): Mun Gi Choi; Nikolas P. Galatsanos; Dan Schonfeld
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Paper Abstract

In this paper, we investigate the relationship between the image restoration and the template matching problems. A new formulation of template matching is presented. According to this formulation the relationship between image restoration and template matching is captured by the observation that the restored template can be used to ascertain its location. As a consequence, the duality between image restoration and template matching is established. An alternative approach is also presented to provide the interpretation of template matching as a special case of blurred image restoration. This approach is based on the observation that template matching can be viewed as image restoration of the degraded version of an unknown image blurred by a linear point spread function -- the template. It is shown that, unlike image restoration, all second order statistics required for the implementation of linear minimum mean-square error (LMMSE) template matching can be easily estimated. The linear minimum mean-square error (LMMSE) template matching is subsequently applied to block-matching motion estimation from degraded image sequences. Our experimental results indicate: First, that LMMSE template matching is superior to traditional block-matching. Second, that there exist certain applications, for example multichannel image sequence restoration, that greatly benefit from LMMSE-based template matching.

Paper Details

Date Published: 27 February 1996
PDF: 12 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233252
Show Author Affiliations
Mun Gi Choi, Illinois Institute of Technology (United States)
Nikolas P. Galatsanos, Illinois Institute of Technology (United States)
Dan Schonfeld, Univ. of Illinois/Chicago (United States)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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