一种改进的一致性扩散图像增强方法

An Improved Coherence Diffusion Method for Image Enhancement

一致性扩散是分析图像中定向结构的重要的预处理步骤,现有的一致性扩散图像增强方法难以识别图像中的弱边界。本文提出了一种改进的扩散方法,该方法构建了一种结合二阶方向导数信息的结构张量,能够精确分析图像中的复杂弱边界,同时与经典结构张量互为补充以检测图像的强边界。在此基础上设计了新的扩散张量,并采用了基于(加性分裂算子AOS)策略的数值计算方法。通过对比实验证明,该文算法的效果良好并具有很高的效率, 实现了在去除噪声的同时精确保护图像中的强弱边界。

Coherence diffusion is an important preprocessing step for analyzing oriented structures in the image. Previous coherence diffusion methods for image enhancement could not recognize weak boundaries. In this paper, an efficient diffusion approach is presented. A new structure tensor integrating the second-order directional derivative information is designed, which can precisely analyze complex weak edges in the image. By combining the proposed structure tensor and the classical one as complementary descriptor, the improved diffusion tensor is constructed to detect strong edges simultaneously. Furthermore, parallel AOS （Additive Operator Splitting） scheme is applied to implement numerical solution, which is faster than usual numerical approach. Promising experimental results of several real images demonstrate that the new diffusion approach can preserve important strong edges and weak edges precisely while removing the noise.