Title :
Robust modeling edge adaptive reduced update Kalman filter
Author :
Hernandez, Victor H. ; Desai, Mita
Author_Institution :
Air Force Inf. Warfare Center, San Antonio, TX, USA
Abstract :
The proposed image reconstruction algorithm is a combination of the robust modeling modified reduced update Kalman filter (RUKF) introduced by Belaifa and Schwartz (1992) and the edge adaptive RUKF by Tekalp et al. (1989), the filter is designed to remove white Gaussian noise as well as salt and pepper noise from a corrupted image while reducing the edge smoothing effect associated with the RUKF. Using the edge adaptive RUKF, the proposed algorithm selects the model that best describes the pixel from the five Kalman filter models available. It uses Kalman edge models when filtering edges and the robust model when filtering non-edge pixels.
Keywords :
Gaussian noise; adaptive Kalman filters; edge detection; filtering theory; image reconstruction; interference suppression; white noise; Kalman edge models; RUKF; edge adaptive reduced update Kalman filter; edge filtering; edge smoothing effect reduction; image reconstruction algorithm; non-edge pixels filtering; robust model; salt and pepper noise; white Gaussian noise removal; Equations; Filtering; Gaussian noise; Image reconstruction; Kalman filters; Noise robustness; Phase noise; Pixel; Predictive models; Smoothing methods;
Conference_Titel :
Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-7646-9
DOI :
10.1109/ACSSC.1996.599098