Paper Info
Title
Impulse noise detection based on robust statistics and genetic programming
Abstract
A new impulse detector design method for image impulse noise is presented. Robust statistics of local pixel neighborhood present features in a binary classification scheme. Classifier is developed through the evolutionary process realized by genetic programming. The proposed filter shows very good results in suppressing both fixed-valued and random-valued impulse noise, for any noise probability, and on all test images.
Year
DOI
Venue
2005
10.1007/11558484_81
ACIVS
Keywords
Field
DocType
binary classification scheme,image impulse noise,present feature,evolutionary process,random-valued impulse noise,local pixel neighborhood,noise probability,genetic programming,robust statistic,impulse noise detection,good result,new impulse detector design,robust statistics,binary classification,design method,impulse noise
Value noise,Median filter,Pattern recognition,Noise measurement,Computer science,Impulse (physics),Genetic programming,Robust statistics,Artificial intelligence,Impulse noise,Gaussian noise
Conference
Volume
ISSN
ISBN
3708
0302-9743
3-540-29032-X
Citations 
PageRank 
References 
7
0.57
7
Authors
2
Name
Order
Citations
PageRank
Nemanja Petrović1522.41
Vladimir S. Crnojevic218617.82