扩展的Chan-Vese模型在噪声图像分割中的应用

Segmentation model using extended Chan-Vese model for noisy image

针对带噪声图像分割结果不理想的现象,提出一种对带不同类型噪声的图像都能进行有效分割的变分模型。首先扩展了Chan-Vese(CV)模型的能量泛函,然后在数值求解过程中,引入一个辅助变量与水平集方法相结合,采用高效和无条件稳定的MOS算法,提高精度和计算效率。对带一定强度噪声的图像进行地分割实验,并与CV变分模型的分割结果进行比较。结果表明,该新变分模型较好地克服了噪声干扰的影响,对带噪图像的分割是有效的,迭代次数少,速度快且提高了目标分割的准确性。

Due to dissatisfactory results of segmenting noisy images, a new variational method for segmenting images corrupted by various noises is presented. First,the energy function of the proposed model based on Chan-Vese（CV） model is modified. Then in order to improve the computing efficiency and the accuracy of the proposed energy minimization problem, a new auxiliary variable is introduced, which is combined with the level set method. By applying the multiplication operator splitting （ MOS ） numerical algorithm, good results are obtained. Finally, compared to the CV model, our results show that the proposed method hurdles the influence of noises, which is effective for segmenting the noisy images. Less iterations are needed,and our approach is faster than the CV model. Furthemore our model has higher object segmentation accuracy.