Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images

This paper presents new clustering-based segmentation algorithms. The proposed switching-based clustering algorithms can minimize salt-and-pepper noise during segmentation without degrading the images’ fine details. The proposed algorithms incorporate the salt-and-pepper noise detection stage into t...

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Bibliographic Details
Published in:Signal, image and video processing Vol. 9; no. 2; pp. 387 - 398
Main Authors: Sulaiman, Siti Noraini, Mat Isa, Nor Ashidi, Yusoff, Intan Aidha, Ahmad, Fadzil
Format: Journal Article
Language:English
Published: London Springer London 01.02.2015
Subjects:
Computer Imaging
Computer Science
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
Image segmentation
Salt-and-pepper noise
Image processing
Clustering
ISSN:1863-1703, 1863-1711
Online Access:Get full text
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Summary:This paper presents new clustering-based segmentation algorithms. The proposed switching-based clustering algorithms can minimize salt-and-pepper noise during segmentation without degrading the images’ fine details. The proposed algorithms incorporate the salt-and-pepper noise detection stage into the clustering algorithm, producing an adaptive technique specifically for segmentation of noisy images. Experimental results show that the proposed switching-based clustering algorithms produce better segmentation with fewer noise effects than conventional clustering algorithms. Quantitative and qualitative analyses show positive results for the proposed switching-based clustering algorithms, which consistently outperform conventional clustering algorithms in segmenting up to 50 % of salt-and-pepper noise density. Thus, these switching-based clustering algorithms can be used as pre- or post-processing task (i.e., segmenting images into regions of interest) in electronic products such as televisions and monitors.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-013-0455-0