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Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).

In this paper, we consider the average trapping time (ATT) on the weighted hierarchical triangle network with primary node iteration. Firstly, the structure with primary node iteration is shown, then ATT is studied and the exact expression is obtained based on the self-similarity of the network. The results illustrate that ATT grows linearly or sublinearly with the iterative times at different weights and with increasing weights. Besides, compared to the network without primary node iteration, the network with primary node iteration is more efficient in random walks and the difference is greater with increasing weights. When the weight is small, there is little difference.

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