Lattice Boltzmann method for filtering and contour detection of the natural images
In this paper, lattice Boltzmann D2Q5 (two dimensions and five discrete velocity directions) and D2Q9 (two dimensions and nine discrete velocity directions) models are used to solve Perona-Malik equation, which is widely used in image filtering. A set of images added three types of noise are processed using these models. Then the processed images are compared in aspects of peak signal to noise ratio (PSNR) and visual effect. The comparison show that two models have almost the same filtering effect. Simultaneously, it is validated that D2Q5 model is more efficient. Other findings are: (1) D2Q5 and D2Q9 models are more effective in dealing with some images than others; (2) salt and pepper noise is relatively difficult to remove compared with gaussian noise and speckle noise; (3) lattice Boltzmann method shows good stability in the image filtering.