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A three-stage approach for segmenting degraded color images: Smoothing, Lifting and Thresholding (SLaT)

A three-stage approach for segmenting degraded color images: Smoothing, Lifting and Thresholding (SLaT)
A three-stage approach for segmenting degraded color images: Smoothing, Lifting and Thresholding (SLaT)
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.
Color spaces, Convex variational models, Multiphase color image segmentation, Mumford–Shah model
1064-8275
1313-1332
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Nikolova, Mila
f3374818-e03c-4f5d-8458-64ec9ce1d73c
Zeng, Tieyong
8bae04dd-2c0d-49f2-898b-30cdc0f5e286
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Nikolova, Mila
f3374818-e03c-4f5d-8458-64ec9ce1d73c
Zeng, Tieyong
8bae04dd-2c0d-49f2-898b-30cdc0f5e286

Cai, Xiaohao, Chan, Raymond, Nikolova, Mila and Zeng, Tieyong (2017) A three-stage approach for segmenting degraded color images: Smoothing, Lifting and Thresholding (SLaT). SIAM Journal on Scientific Computing, 72 (3), 1313-1332. (doi:10.1007/s10915-017-0402-2).

Record type: Article

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.

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More information

Accepted/In Press date: 23 February 2017
e-pub ahead of print date: 10 March 2017
Published date: 1 September 2017
Keywords: Color spaces, Convex variational models, Multiphase color image segmentation, Mumford–Shah model

Identifiers

Local EPrints ID: 438760
URI: http://eprints.soton.ac.uk/id/eprint/438760
ISSN: 1064-8275
PURE UUID: a964c4a7-b5b8-4b70-9862-c4a90dc6755b

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Date deposited: 24 Mar 2020 17:30
Last modified: 29 Jun 2020 16:30

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Contributors

Author: Xiaohao Cai
Author: Raymond Chan
Author: Mila Nikolova
Author: Tieyong Zeng

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