DocumentCode :
327930
Title :
Structure preserving noise filtering of images using explicit local segmentation
Author :
Seemann, Torsten ; Tischer, Peter
Author_Institution :
Sch. of Comput. Sci. & Software Eng., Monash Univ., Clayton, Vic., Australia
Volume :
2
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
1610
Abstract :
The trend in modern image noise filtering algorithms has been toward structure preservation by using only those neighbouring pixels which are similar to the current pixel in some way. In this paper we introduce a technique, call FUELS (filtering using explicit local segmentation), which explicitly segments the m × m region encompassing the current pixel and filters using only those pixels from the same segment. By exploiting mask overlap an effective mask size of(2m-1)×(2m-1) is obtained, as well as robustness in regions which do not fit the image model. The algorithm can be iterated, and our results show FUELS to outperform existing algorithms both quantitatively and qualitatively
Keywords :
computer vision; filtering theory; image segmentation; iterative methods; noise; explicit local segmentation; image model; image segmentation; image structure preserving; iterative method; mask overlapping; neighbouring pixels; noise filtering; Additive noise; Australia; Computer science; Filtering; Fuels; Image edge detection; Image segmentation; Nonlinear filters; Pixel; Software engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.712023
Filename :
712023
Link To Document :
بازگشت