A multiphase image segmentation based on fuzzy membership functions and L1-norm fidelity. (English) Zbl 1353.65015
This paper presents a novel piecewise constant image segmentation model based on fuzzy membership functions and L1-norm fidelity. Observing that the L1-norm is more robust to impulse noise and outliers and can better preserve contrast, in this paper, the authors propose a variational multiphase fuzzy segmentation model based on L1-norm fidelity and fuzzy membership functions. ADMM [D. Gabay and B. Mercier, Comput. Math. Appl. 2, 17–40 (1976; Zbl 0352.65034)], [R. Glowinski and A. Marroco, Rev. Franc. Automat. Inform. Rech. Operat., R 9, No. 2, 41–76 (1975; Zbl 0368.65053)] is applied to derive an efficient numerical algorithm, in which each subproblem has a closed-form solution. The proposed method is closely related to the method given in [Y. He et al., Pattern Recognition 45, No. 9, 3463–3471 (2012; Zbl 1242.68360)].
Reviewer: H. P. Dikshit (Bhopal)
MSC:
| 65D18 | Numerical aspects of computer graphics, image analysis, and computational geometry |
Keywords:
image segmentation; fuzzy membership function; L1-norm; ADMM; segmentation accuracy; algorithmReferences:
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