引入分数阶微分的局部高斯分布拟合能量模型

局部高斯分布拟合能量(LGDF)模型缺乏全局信息,对初始轮廓曲线选取较敏感,特别在分割弱边缘和弱纹理区域图像时,容易陷入局部极值,对噪声的鲁棒性不好.针对上述问题,文中提出引入分数阶微分的LGDF模型.在LGDF模型中引入全局的Grümwald-Letnikov(G-L)分数阶梯度拟合项,增强弱边缘和弱纹理区域的梯度信息,提高对初始轮廓曲线和噪声的鲁棒性.采用自适应权重函数确定全局项和局部项的系数,提高对灰度不均匀图像的分割效率和分割精度.根据图像的梯度模值、信息熵和对比度构建自适应分数阶阶次的函数,提高分割效率.理论分析和实验均表明,文中模型可以用于灰度不均匀、弱纹理、弱边缘图像的分割.合成图像和真实图像的实验表明文中模型可以提高图像的分割精度和效率.

Local Gaussian Distribution Fitting Energy Model with Fractional Differential

Local Gaussian distribution fitting energy(LGDF) model lacks global information and is sensitive to the selection of initial contour curve. Especially in the segmentation of images with weak edges and textures, it is easy to fall into the local extremum and is poorly robust to noise. To solve the problems, an LGDF model with fractional differential is proposed. A global Grümwald-Letnikov fractional gradient fitting term is introduced into LGDF model to enhance the gradient information of the weak edge and texture regions and improve the robustness to initial contour curve and noise. The coefficients of global and local terms are determined by adaptive weighting function to improve the efficiency and accuracy of gray-scale inhomogeneous image segmentation. The adaptive fractional order function is constructed according to gradient modulus, information entropy and contrast of the image to improve the segmentation efficiency. Both theoretical analysis and experiments show that the model can be used for the segmentation of the gray-scale inhomogeneous images and the images with weak texture and weak edge. Experiments on synthetic and real images show that the proposed model improves the accuracy and efficiency of image segmentation.