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Variational image segmentation model coupled with image restoration achievements

Variational image segmentation model coupled with image restoration achievements
Variational image segmentation model coupled with image restoration achievements
Image segmentation and image restoration are two important topics in image processing with a number of important applications. In this paper, we propose a new multiphase segmentation model by combining image restoration and image segmentation models. Utilizing aspects of image restoration, the proposed segmentation model can effectively and robustly tackle images with a high level of noise or blurriness, missing pixels or vector values. In particular, one of the most important segmentation models, the piecewise constant Mumford-Shah model, can be extended easily in this way to segment gray and vector-valued images corrupted, for example, by noise, blur or information loss after coupling a new data fidelity term which borrowed from the field of image restoration. It can be solved efficiently using the alternating minimization algorithm, and we prove the convergence of this algorithm with three variables under mild conditions. Experiments on many synthetic and real-world images demonstrate that our method gives better segmentation results in terms of quality and quantity in comparison to other state-of-the-art segmentation models, especially for blurry images and those with information loss.
Image restoration, Image segmentation, Mumford-Shah model, Piecewise constant
0031-3203
2029-2042
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee

Cai, Xiaohao (2015) Variational image segmentation model coupled with image restoration achievements. Pattern Recognition, 48 (6), 2029-2042. (doi:10.1016/j.patcog.2015.01.008).

Record type: Article

Abstract

Image segmentation and image restoration are two important topics in image processing with a number of important applications. In this paper, we propose a new multiphase segmentation model by combining image restoration and image segmentation models. Utilizing aspects of image restoration, the proposed segmentation model can effectively and robustly tackle images with a high level of noise or blurriness, missing pixels or vector values. In particular, one of the most important segmentation models, the piecewise constant Mumford-Shah model, can be extended easily in this way to segment gray and vector-valued images corrupted, for example, by noise, blur or information loss after coupling a new data fidelity term which borrowed from the field of image restoration. It can be solved efficiently using the alternating minimization algorithm, and we prove the convergence of this algorithm with three variables under mild conditions. Experiments on many synthetic and real-world images demonstrate that our method gives better segmentation results in terms of quality and quantity in comparison to other state-of-the-art segmentation models, especially for blurry images and those with information loss.

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More information

Accepted/In Press date: 13 January 2015
e-pub ahead of print date: 19 January 2015
Published date: 1 June 2015
Keywords: Image restoration, Image segmentation, Mumford-Shah model, Piecewise constant

Identifiers

Local EPrints ID: 438756
URI: http://eprints.soton.ac.uk/id/eprint/438756
ISSN: 0031-3203
PURE UUID: 40662c96-e50d-4d6c-991e-7868f2c8b93e

Catalogue record

Date deposited: 24 Mar 2020 17:30
Last modified: 19 May 2020 17:02

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