一种基于局部符号差能量的非局部分割模型

针对灰度非均匀的图像,提出一种基于局部符号差能量的非局部图像分割模型。该模型包含基于局部符号差能量的数据驱动项和非局部全变分正则项,具有局部可分离性和全局一致性的特点。由于本文模型是凸的,因此在数值实现上可以采用split-Bregman迭代算法,具有较快的运算速度。同经典的基于局部区域的主动轮廓分割模型相比,该方法具有以下优点:(1)该模型受初始化的影响很小;(2)采用split-Bregman迭代算法,运算速度更快;(3)能够对具有细密纹理和具有弱边缘目标的图像进行正确分割。实验结果表明,该模型对灰度非均匀图像能够进行较准确的分割,相比其他模型具有更好的鲁棒性。

A nonlocal segmentation model based on local signed difference energy

We propose a nonlocal image segmentation model based on local signed difference (LSD) energy for images with intensity inhomogeneity. The model consists of a data driven term based on LSD energy and a nonlocal total variation regularization term, and possesses local separability and global consistency. Due to the convexity of the model, the split Bregman iteration algorithm can be used in the numerical implementation, thus having a fast speed. Compared with the classical local region based active contour models (ACMs), the proposed model has several advantages as follows: 1) it is less sensitive to the initialization; 2) it is more efficient by using the split Bregman iteration algorithm; 3) it can correctly segment images with fine textures and weak object boundaries. Experimental results show that the model can segment images with intensity inhomogeneity correctly and works more robust than other models.