各向异性图像扩散的多重网格格子波尔兹曼方法

为提高基于格子波尔兹曼(Lattice Boltzmann,LB) 模型图像降噪方法的计算效率和精度，提出了一种多重网格LB (multigrid LB,M-LB) 模型的降噪方法，即通过不同尺度网格的LB 模型实现各向异性图像扩散，在图像变化剧烈的区域采用较细尺度的网格，而在图像变化缓慢的区域采用较粗尺度的网格. 为验证M-LB 方法针
对斑点噪声抑制的效果与效率，对自然图像、合成图像、医学超声图像进行降噪处理，分别与现有的一种多重网格扩散方法和两种LB方法进行对比．实验结果显示，M-LB 方法较其他3 种方法抑制斑点噪声效果更好，降噪处理效率更高．

Multi-grid Lattice Boltzmann Method for Anisotropic Image Diffusion

To augment precision and computing efficiency of traditional Lattice Boltzmann (LB) methods in speckle reduction, this paper proposes a multi-grid LB (M-LB) method. The method automatically realizes anisotropic image diffusion of LB by applying different scales of grid: fine-grids are applied to regions with
obvious gradient change, and coarse-grids applied to regions with tiny gradient change. Experiments were carried out to test the method for speckle noise reduction. The proposed M-LB method was compared to an existing multi-grid method and two traditional LB methods. Natural images, composite images and medical ultrasound images were used. The experiments show that the M-LB method can achieve better denoising results and higher computation efficiency.