A binary level set variational model with L1 data term for image segmentation

doi:10.1016/j.sigpro.2018.08.017

Return

A binary level set variational model with L1 data term for image segmentation

2019-02-01

PRE

刘阳 (Yang Liu)

何

何传江 (Chuanjiang He) *

DOI：10.1016/j.sigpro.2018.08.017

Original

Original request for help

Save

Abstract

En 中文

In this paper, we propose a variational model in binary level set framework for segmenting two-phase images with impulse noise. In this model, the contour is implicitly represented by the discontinuity of a binary level set function instead of the zero-level set. The energy functional consists of three terms, i.e., the data term that is defined by L-1-norm metric, the regularization term that is defined by Dirichlet energy of level set function, and the penalty term that punishes the deviation of level set function from binary function. We design a three-step time-splitting scheme to solve the gradient descent flow of the proposed model efficiently, in which the flow equation is divided into three differential equations that are solved sequentially and alternatively until convergence. We present some experimental results that are performed on simulated and real images, which demonstrate that our model is very robust to impulse noise and has the best performance compared with five relevant models. (C) 2018 Elsevier B.V. All rights reserved.

Keywords:

Image segmentation

Variational model

Level set method

Binary function

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.