Abstract
The lattice Boltzmann method has attracted more and more attention as an alternative numerical scheme to traditional numerical methods for solving partial differential equations and modeling physical systems. The idea of the lattice Boltzmann method is to construct a simplified discrete microscopic dynamics to simulate the macroscopic model described by the partial differential equations. In this paper, we present the lattice Boltzmann models for nonlinear diffusion filtering. We show that image feature selective smoothing can be achieved by making the relaxation parameter in the lattice Boltzmann equation be image feature and direction dependent. The models naturally lead to the numerical algorithms that are easy to implement. Experimental results on both synthetic and real images are described.
The research of the authors was supported by ARO Grant DAA HO 49610326, ONR Grant N00014-90-J1343, and DEPSCoR Grant N00014-97-10806.
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Jawerth, B., Lin, P., Sinzinger, E. (1999). Lattice Boltzmann Models for Nonlinear Diffusion Filtering. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48236-9_25
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DOI: https://doi.org/10.1007/3-540-48236-9_25
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