An improvement of unsupervised design method for weighted median filters using GA
Yoshiko Hanada, Mitsuji Muneyasu, Akira Asano · 2009
Design of both a suitable window shape and appropriate weights in weighted median filters is one of important problems. Hitherto, unsupervised design methods of the filters by using simulated annealing (SA) or genetic algorithm (GA) have been proposed for texture images corrupted by impulse noise. These techniques estimate the optimal window shape and the optimal filter weights separately, and they have been shown to perform as well as a supervised method. In this paper, we propose a new approach which optimize both the window shape and the weight at the same time to design more sophisticated filters. We apply GA and adopt rank-ordered logarithmic difference (ROLD) statistics as objective function to design a weighted median filter. Through experiments, it was shown that our new approach outperformed compared to conventional methods.