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Image-Guided Filtering Method Constrained by Anisotropic Gaussian Side Window Kernel

An anisotropic, image-guided technology, applied in the field of image processing, can solve the problems of limited rectangular window width and small edge resolution, and achieve the effects of reduced complexity, clear edges, and simple filtering methods.

Active Publication Date: 2021-10-22
XIAN UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in order to achieve the ideal edge preservation effect, the side window filtering method will be limited by the width of the rectangular window. The larger the width, the smaller the edge resolution.

Method used

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  • Image-Guided Filtering Method Constrained by Anisotropic Gaussian Side Window Kernel
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  • Image-Guided Filtering Method Constrained by Anisotropic Gaussian Side Window Kernel

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Experimental program
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Embodiment 1

[0065] exist figure 1 Among them, the image-guided filtering method constrained by the anisotropic Gaussian side window kernel of the present embodiment consists of the following steps:

[0066] (1) Construct an anisotropic Gaussian kernel

[0067] Construct the anisotropic Gaussian kernel g as follows σ,ρ,θ (n):

[0068]

[0069]

[0070] Where n is the local pixel position in the filter window, θ is the rotation angle based on the y-axis, θ∈(0,π], σ is the Gaussian scale, σ∈(1,6], the value of σ in this embodiment is 3, ρ is the anisotropy factor, ρ∈(1,12], the value of ρ in this embodiment is 6, R θ is the rotation matrix with direction θ.

[0071] (2) Determine the anisotropic Gaussian side window kernel

[0072] Determine the anisotropic Gaussian side window kernel N according to formula (3) θ :

[0073] N θ ={n|xcosθ+ysinθ>0,g σ,ρ,θ (n)>ε,n=[x,y]} (3)

[0074] Where x and y are non-negative integers, ε is a threshold, ε∈[0.00005,0.00015], and the value of...

Embodiment 2

[0117] The image-guided filtering method constrained by the anisotropic Gaussian side window kernel of the present embodiment consists of the following steps:

[0118] (1) Construct an anisotropic Gaussian kernel

[0119] Construct the anisotropic Gaussian kernel g as follows σ,ρ,θ (n):

[0120]

[0121]

[0122] Where n is the local pixel position in the filter window, θ is the rotation angle based on the y-axis, θ∈(0,π], σ is the Gaussian scale, σ∈(1,6], the value of σ in this embodiment is 1.5, ρ is the anisotropy factor, ρ∈(1,12], the value of ρ in this embodiment is 1.5, R θ is the rotation matrix with direction θ.

[0123] (2) Determine the anisotropic Gaussian side window kernel

[0124] Determine the anisotropic Gaussian side window kernel N according to formula (3) θ :

[0125] N θ ={n|xcosθ+ysinθ>0,g σ,ρ,θ (n)>ε,n=[x,y]} (3)

[0126] Where x and y are non-negative integers, ε is a threshold, ε∈[0.00005,0.00015], and the value of ε in this embodiment is...

Embodiment 3

[0129] The image-guided filtering method constrained by the anisotropic Gaussian side window kernel of the present embodiment consists of the following steps:

[0130] (1) Construct an anisotropic Gaussian kernel

[0131] Construct the anisotropic Gaussian kernel g as follows σ,ρ,θ (n):

[0132]

[0133]

[0134] Where n is the local pixel position in the filtering window, θ is the rotation angle based on the y-axis, θ∈(0,π], σ is the Gaussian scale, σ∈(1,6], the value of σ in this embodiment is 6, ρ is the anisotropy factor, ρ∈(1,12], the value of ρ in this embodiment is 12, R θ is the rotation matrix with direction θ.

[0135] (2) Determine the anisotropic Gaussian side window kernel

[0136] Determine the anisotropic Gaussian side window kernel N according to formula (3) θ :

[0137] N θ ={n|xcosθ+ysinθ>0,g σ,ρ,θ (n)>ε,n=[x,y]} (3)

[0138] Where x and y are non-negative integers, ε is a threshold, ε∈[0.00005,0.00015], and the value of ε in this embodiment is 0...

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Abstract

An image-guided filtering method constrained by an anisotropic Gaussian side window kernel, which consists of constructing an anisotropic Gaussian side window kernel, determining the anisotropic Gaussian side window kernel, determining the anisotropic Gaussian side window Kernel weight matrix, construction of anisotropic Gaussian side window kernel-guided filter, selection of the optimal filtering result, and determination of the filtering result consist of seven steps. Since the present invention adopts the anisotropic Gaussian edge window kernel, it has a strong edge preservation effect, and compared with the traditional box filtering method, the image processed by the present invention has clear edges and good retention. In the guided filtering process, the weight coefficient of the anisotropic Gaussian side window kernel filter is used for weighted filtering, which has better edge preservation. A linear filtering method is adopted, which greatly reduces the complexity of the filtering method. The invention has the advantages of simple filtering method, convenient operation, good high-resolution edge retention and the like, and can be used for image filtering processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to image edge processing. [0002] technical background [0003] Edge-preserving filtering methods are often used in preprocessing operations of computer vision and graphics and image processing, and the quality of the results directly affects many subsequent operations. The edge-preserving filtering method not only pays attention to the smoothing of the image, but also pays attention to maintaining the edge details. Traditional image smoothing methods focus on the smoothing effect, resulting in the loss of edge details after the image is filtered. In order to solve the technical problem of loss of edge details, many edge-preserving filtering methods have been proposed and widely used in computer animation, digital photography and other technical fields. [0004] Image filtering methods based on local filters use adjacent pixels to calculate new pixels, such as...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T2207/20192G06T5/70
Inventor 王富平吉聪聪陈鹏博公衍超高梓铭刘颖韦同胜刘卫华李兴
Owner XIAN UNIV OF POSTS & TELECOMM