基于人类视觉模型的各向异性扩散滤波方法

针对各向异性扩散模型中扩散系数的梯度阈值确定问题进行了研究,提出了一种基于人类视觉模型的各向异性扩散滤波器。首先在PM方程的基础上研究了各向异性扩散系数中梯度阈值的取值问题。根据仿生学原理,分析了韦伯比曲线,将图像根据背景亮度划分为不同的区域,分别采用不同的公式计算梯度阈值。然后讨论了改进后算法在8邻域内的离散实现问题。仿真结果表明,与传统的恒常梯度阈值扩散模型相比,改进后的算法在有效保留图像重要信息的同时对噪声的抑制效果更为理想。

Anisotropic diffusion image smoothing method based on human visual model

We propose an improved anisotropic diffusion method based on the human visual model to gradient threshold for the diffusion coefficients.First,we studied the evaluation of gradient thresholds for anisotropic diffusion coefficient based on the primal PM equation.Through analyzing Weber’s law according to the bionics principle and based on the background,the image brightness was divided into different regions.The gradient thresholds for the different areas were calculated according to the different equations.Then,we discussed the discrete implementation of the proposed method for the eight neighborhoods.The simulation results indicate that the proposed method not only preserves the image information better,but also suppression the noise more satisfactory.