基于小波变换的权重自适应图像分割模型

针对灰度不均匀且含噪声图像的分割问题，提出了全局和局部灰度信息的权重参 数自适应水平集分割模型。首先，利用图像的全局和局部灰度信息构造全局能量项和局部能量 项;然后,利用小波变换和小波阈值去噪方法，构造对噪声不敏感的边缘信息刻画矩阵，定义包含 图像边缘信息的自适应权重系数矩阵；最后，利用定义的权重系数矩阵组合全局和局部能量项， 得到分割模型的能量泛函。使用变分法得到了水平集函数演化方程，利用有限差分法实现数值 求解。实验结果表明，该模型兼有 Chan-Vese 模型和 Local Binary Fitting 模型的优点，能够有效 地分割灰度不均匀含噪图像，并对活动轮廓曲线的初始位置和初始形状具有很强的鲁棒性。

Adaptive weights image segmentation model based on wavelet transform

In this paper, in response to the question of the segmentation of images with intensity inhomogeneity and noise, a new adaptive weights image segmentation model was proposed based on the combination of the local and global region intensity information. Firstly, the local and global energy functionals were constructed based on the local and global image intensity information respectively. Secondly, a new adaptive weights function based on the denoising methods of wavelet transform and wavelet threshold was defined by constructing the edge characterization of matrix insensitive to noise. Finally, the defined weights function was utilized to combine local and global terms adaptively to obtain the energy functional of the proposed model. The model’s level-set function was deduced from the variational method, and the finite difference method was employed to realize numerical solution. Experimental results show that the proposed model, which combines the advantages of the Chan-Vese model and the Local Binary Fitting model, can effectively segment images with intensity inhomogeneity and noise and is robust to the positions and shapes of initial contour of evolution curve.