各向异性图像扩散的多重网格格子波尔兹曼方法

Multi-grid Lattice Boltzmann Method for Anisotropic Image Diffusion

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.

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.