Designing optimal decision-directed locally adaptive linear filters

Octavian Valeriu Sarca; Jaakko T. Astola

doi:10.1117/12.304614

6 April 1998 Designing optimal decision-directed locally adaptive linear filters

Octavian Valeriu Sarca, Jaakko T. Astola

Proceedings Volume 3304, Nonlinear Image Processing IX; (1998) https://doi.org/10.1117/12.304614
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

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.

Citation Download Citation

Octavian Valeriu Sarca and Jaakko T. Astola "Designing optimal decision-directed locally adaptive linear filters", Proc. SPIE 3304, Nonlinear Image Processing IX, (6 April 1998); https://doi.org/10.1117/12.304614

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