基于梯度信息的自适应邻域噪声图像分割模型

Noise Image Segmentation Model with Adaptive Neighborhood Based on Gradient Information

现有分割模型多数不能兼顾概率噪声图像和非均匀图像的分割精度,为此,构建一种改进的活动轮廓模型。引入新的邻域拉伸变形机制,使用图像梯度引导邻域变形,以降低无关信息和噪声点对分割结果的影响。将局部相关性系数引入能量泛函,使模型可以准确辨别信息价值较高的像素点。在此基础上,通过最小化能量函数驱动活动分割轮廓向目标边缘演化。实验结果表明,该模型可有效分割具有弱边界性质的概率噪声图像,且分割精度高于自适应LCV模型、LCK模型等。

An improved active contour model is proposed to solve the problem that most of the existing segmentation model can not take into account the segmentation accuracy of the probability noise image and the inhomogeneous image.Firstly,the new neighborhood tensile deformation mechanism is introduced, and the image gradient is used to guide the neighborhood deformation to reduce the effect of the independent information and the noise point on the segmentation results. Secondly,the local correlation coefficient is introduced into the energy function, so that the model can accurately identify the important pixels. Finally,by minimizing the energy function,the active contour is driven to the target edge.Experimental results show that,the proposed model can effectively segment the probability noise with weak boundary properties, and its segmentation accuracy is higher than the adaptive LCV model,LCK model and other models.