Image de-noising method based on lattice Boltzmann model
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A grid and model technology, applied in the field of image processing, can solve problems such as low efficiency of calculation process, small step size, unsuitable for real-time image processing, etc.
Inactive Publication Date: 2010-03-17
SHANGHAI UNIV
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Although this explicit finite difference algorithm is easy to implement, due to the limitation of stability, the iterative step size is very small (such as 1 / 4)
In this way, in order to reach the expected diffusion time, many iterations are required, and the entire calculation process is inefficient, making it unsuitable for real-time image processing.
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Embodiment 1
[0068] Example 1: Grayscale Image Denoising Processing
[0069] The effect of processing the grayscale image is shown in Figure 4. 4-1, 4-2, 4-3, and 4-4 are the original Lena image in turn; add the noised Lena image with white noise with a mean value of 0 and a variance of 0.01; based on D2Q5 lattice Boltzmann model denoising method (parameters are: threshold 25, step size 5, number of iterations 10) processed image; based on D2Q9 lattice Boltzmann model denoising method (parameters are: threshold 25, Step size 5, number of iterations 8) Processed image. Taking the denoising method based on the D2Q9 lattice Boltzmann model as an example, the steps are as follows:
[0070] (1), input the initial image I(x, 0), its gray value is set as the number of particles on the node,
[0071] (2), according to the number of neighborhood directions of the model, set the initial equilibrium function I of each direction i eq (x,0),
[0072] I i ...
Embodiment 2
[0083] Embodiment 2: color image denoising processing
[0084] The effect of processing color images is shown in Figure 5. 5-1, 5-2, 5-3, and 5-4 are the original pepper images in turn; the noise-added pepper image with white noise with a mean value of 0 and a variance of 0.01 is added; based on D2Q5 The denoising method of the lattice Boltzmann model (parameters are: threshold 100, step size 5, number of iterations 5) processed image; based on the denoising method of D2Q9 lattice Boltzmann model (parameters are: threshold 100, step length 5, number of iterations 4) processed image. Taking the denoising method based on the D2Q5 lattice Boltzmann model as an example, the steps are as follows:
[0085] (1), let each component image of color image be I j (x,0)(j=1,2,3)
[0086] (2), the initial grayscale of each direction of the component image is I ji (x, 0), set the equilibrium function of each direction for the component image:
[0087] I ...
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Abstract
The invention discloses an image de-noising method based on a lattice Boltzmann model. The method comprises the following steps: (1) inputting an initial image I (x,0); (2) setting the initial equilibrium state function Ii<eq>(x,0) of each action direction in a two-dimensional lattice Boltzmann evolution equation; (3) determining the iteration frequency N and the iteration step-length C of the lattice Boltzmann evolution equation; (4) traversing the image to calculate the relaxation factor omega in the lattice Boltzmann evolution equation; (5) calculating the migration process of the lattice Boltzmann model; (6) calculating the action process of the lattice Boltzmann model; (7) updating the equilibrium distribution function as Ii<eq>(x,n); and (8) judging whether achieving the iteration frequency N; if achieving N times, outputting the processed image I (x,N). The method can suppress the noise of the image and effectively protect the edge of the image. The de-noising quality of the image can be enhanced, and iterative calculation of large step-length can be realized, thus enhancing the efficiency of de-noising processing effectively.
Description
technical field [0001] The invention relates to a method for solving a nonlinear diffusion equation based on a lattice Boltzmann model (lattice boltzmann model, LBM) to realize image denoising, and belongs to the field of image processing. technical background [0002] Currently, image denoising using nonlinear diffusion models is an important application of partial differential equations in the field of image processing. It can effectively protect the edge of the image while suppressing image noise. The use of nonlinear diffusion model for image denoising was first proposed by Perona and Malik in 1990. Two years later, Catte et al. made improvements in the theory and implementation of the model. In 1998, Weickert improved the texture image by introducing the diffusion tensor. smoothing effect. However, due to the inherent discontinuity of digital images, the nonlinear partial differential equation obtained by the mathematical model, and the huge amount of image data, the ...
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