Paper Info
Title
A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)
Abstract
In this paper, we propose a Smoothing, Lifting and Thresholding (SLaT) method with three stages for multiphase segmentation of color images corrupted by different degradations: noise, information loss and blur. At the first stage, a convex variant of the Mumford–Shah model is applied to each channel to obtain a smooth image. We show that the model has unique solution under different degradations. In order to properly handle the color information, the second stage is dimension lifting where we consider a new vector-valued image composed of the restored image and its transform in a secondary color space to provide additional information. This ensures that even if the first color space has highly correlated channels, we can still have enough information to give good segmentation results. In the last stage, we apply multichannel thresholding to the combined vector-valued image to find the segmentation. The number of phases is only required in the last stage, so users can modify it without the need of solving the previous stages again. Experiments demonstrate that our SLaT method gives excellent results in terms of segmentation quality and CPU time in comparison with other state-of-the-art segmentation methods.
Year
DOI
Venue
2015
10.1007/s10915-017-0402-2
J. Sci. Comput.
Keywords
Field
DocType
Mumford–Shah model, Convex variational models, Multiphase color image segmentation, Color spaces
Computer vision,Secondary color,Mathematical optimization,Color space,Scale-space segmentation,CPU time,Segmentation,Image segmentation,Smoothing,Artificial intelligence,Thresholding,Mathematics
Journal
Volume
Issue
ISSN
abs/1506.00060
3
1573-7691
Citations 
PageRank 
References 
4
0.44
36
Authors
4
Name
Order
Citations
PageRank
Xiaohao Cai1857.96
Raymond H. Chan21549151.24
Mila Nikolova31792105.71
Tieyong Zeng487448.72