基于水平集的新型彩色图像分割算法

摘要

由于考虑的泛函变分形式是非凸性质,向量值图像分割模型的计算结果经常会陷入局部最小值.基于活动轮廓的向量值图像的全局图像分割方法,以新型变分形式将向量值图像分割和图像去噪融入具有全局极小能力泛函框架中.新模型具有容易构造和较少计算量的特点,对比经典的水平集方法,可以避免繁琐的距离重复化水平集过程.通过对人工图像和真实图像进行分析,验证新方法具有更好的图像分割效果.%Since the functional form in consideration is of non-convex variational nature, the calculation results of the image segmentation model often fall into local minimum. Based on the global vector-valued image segmentation of active contour, the global vector-valued image segmentation and image denoising were integrated in a new variational form within the framework of global minimum. The new model was easy to construct and of less computation. Compared to the classical level set method, tedious repetition of the level set could be avoided. With the analyses on artificial images and real images, the new method is verified to have better segmentation results.