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Proceedings Paper

Designing optimal decision-directed locally adaptive linear filters
Author(s): Octavian Valeriu Sarca; Jaakko T. Astola
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Paper Abstract

Local adaptation of the filter parameters is a well known solution that overcomes the fundamental tradeoff between noise rejection and image detail preservation. A remarkable class of local adaptation is the one that uses one or more decision filters to choose between several possible sets of parameters for the main filter. However, the design of these filters is typically based on ad-hoc solutions. Usually, if training- based design is required, the decision filters are chosen a priori and then the optimization is performed only on the main filter. The paper shows that, if the main filters are linear or polynomial, then a closed form expression of the mean- square-error can be derived and thus, the whole structure can undergo optimization. Next, a particular case is considered, that is when the decision filters are made by thresholding the output of linear or polynomial filters. A practical design procedure is developed for this case; it uses an efficient gradient based method that can reach the solution in few iterations. Experimental results are used to compare the optimized filter against ad-hoc solutions and non-adaptive optimal filters.

Paper Details

Date Published: 6 April 1998
PDF: 11 pages
Proc. SPIE 3304, Nonlinear Image Processing IX, (6 April 1998); doi: 10.1117/12.304614
Show Author Affiliations
Octavian Valeriu Sarca, Tampere Univ. of Technology (Finland)
Jaakko T. Astola, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 3304:
Nonlinear Image Processing IX
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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