Speckle noise reduction using an interval type-2 fuzzy sets filter

Author

Bigand, Andre ; Colot, Olivier

Author_Institution

LISIC, ULCO, Calais, France

fYear

2012

fDate

6-8 Sept. 2012

Firstpage

60

Lastpage

66

Abstract

Fuzzy sets which capture the meaning representation of linguistic variables have been widely used in image processing in the last decades. Fuzzy sets are associated with vagueness which is type 1 uncertainty. Interval-valued fuzzy sets (IVFS) are associated with type 2 semantic uncertainty. Indeed, the length of the interval provides the "`non-specifity" measure for IVFS. We investigate this particular information measure applied to low-level image processing. This method can be used for both smoothing and noise filtering and applications in speckle noise reduction show the interest of this concept.

Keywords

computer vision; filtering theory; fuzzy set theory; image denoising; smoothing methods; IVFS; computer vision; information measure; interval type-2 fuzzy set filter; interval-valued fuzzy sets; linguistic variables; low-level image processing; noise filtering; nonspecifity measure; smoothing method; speckle noise reduction; type-1 uncertainty; type-2 semantic uncertainty; Entropy; Fuzzy sets; Image restoration; Noise; Speckle; Uncertainty; Interval type-2 fuzzy sets; computer vision; low-level image processing; speckle filtering;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (IS), 2012 6th IEEE International Conference

Conference_Location

Sofia

Print_ISBN

978-1-4673-2276-8

Type

conf

DOI

10.1109/IS.2012.6335115

Filename

6335115