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Cartoon-texture evolution for two-region image segmentation

Laura Antonelli (), Valentina De Simone () and Marco Viola ()
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Laura Antonelli: National Research Council (CNR)
Valentina De Simone: University of Campania “Luigi Vanvitelli”
Marco Viola: University of Campania “Luigi Vanvitelli”

Computational Optimization and Applications, 2023, vol. 84, issue 1, No 2, 5-26

Abstract: Abstract Two-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al. (SIAM J Appl Math 66(5):1632–1648, 2006) designed a model well suited for smooth images. One drawback of this model is that it may produce a bad segmentation when the image contains oscillatory components. Based on a cartoon-texture decomposition of the image to be segmented, we propose a new model that is able to produce an accurate segmentation of images also containing noise or oscillatory information like texture. The novel model leads to a non-smooth constrained optimization problem which we solve by means of the ADMM method. The convergence of the numerical scheme is also proved. Several experiments on smooth, noisy, and textural images show the effectiveness of the proposed model.

Keywords: Image segmentation; Cartoon-texture decomposition; Non-smooth optimization; ADMM method (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10589-022-00387-7

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