矢量图像去噪的格子波尔兹曼方法

摘要： 针对矢量图像数据量大，基于传统非线性扩散模型的算法效率低和难以实现并行化的缺点，该文通过在格子波尔兹曼模型的松弛因子中嵌入矢量图像的边缘特征，并定义新的平衡态分布函数，实现矢量图像的非线性扩散去噪. 此方法在保证稳定性的情况下，能实现大步长迭代计算以提高计算效率. 随后，通过二维扩散问题的数值仿真，定量评价了平衡态分布函数对计算精度和效率的影响. 为验证此种方法，对分别受到加性高斯白噪声和脉冲噪声的彩色图像进行了去噪实验，结果显示在图像处理质量和计算效率方面，该文方法都优于加性算子分裂算法.

Lattice Boltzmann Method for Vector Image Denoising

We present a new lattice Boltzmann method for vector image nonlinear diffusion by embedding vector image’s edge feature into the relaxation parameter of the Lattice Boltzmann equation and designing high efficient equilibrium distributions. This overcomes the problems of poor efficiency and the difficulty in parallelization of the traditional numerical methods using nonlinear diffusion model for vector image denoising. To keep the algorithm stable, the method can iterate with large step to improve computation efficiency. Influence of equilibrium distribution on computation accuracy and efficiency is evaluated in the numerical simulation of second dimensional diffusion problem. To illustrate effectiveness of the method, color images contaminated by Gaussian noise and impulsive noise are tested. Experimental results and the analysis show that computation accuracy and efficiency of the proposed method are better than that of the traditional numerical method such as additive operator splitting (AOS) algorithm