DocumentCode
1752250
Title
Design of weighted order statistic filters by training-based optimization
Author
Koivisto, Pertti ; Huttunen, Heikki
Author_Institution
Signal Process. Lab., Tampere Univ. of Technol., Finland
Volume
1
fYear
2001
fDate
2001
Firstpage
40
Abstract
This paper demonstrates how weighted order statistic filters can be designed using training-based optimization. The design method utilizes supervised learning with simulated annealing as the learning rule. In addition, the efficiency and flexibility of the presented method are studied through experiments
Keywords
circuit optimisation; image processing; learning (artificial intelligence); median filters; network synthesis; nonlinear filters; simulated annealing; design method efficiency; heavy-tailed noise; impulsive noise; learning rule; noise distribution; nonlinear filters; simulated annealing; supervised learning; training image; training-based optimization; weighted median filters; weighted order statistic filter design; Design methodology; Design optimization; Filters; Laboratories; Optimization methods; Signal design; Signal processing; Simulated annealing; Statistics; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing and its Applications, Sixth International, Symposium on. 2001
Conference_Location
Kuala Lumpur
Print_ISBN
0-7803-6703-0
Type
conf
DOI
10.1109/ISSPA.2001.949770
Filename
949770
Link To Document