Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Adaptive edge preserving denoising method based on anisotropic diffusion model

A diffusion model and edge preservation technology, applied in the field of image processing, can solve the problem that the diffusion function of the PM model cannot converge quickly, is prone to "block effect, and needs to be improved, and achieves improved image fidelity, good effect, and good diffusion." effect of speed

Pending Publication Date: 2022-02-08
ANHUI UNIVERSITY
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The anisotropic diffusion model can adaptively select the diffusion direction during the diffusion process. This feature ensures that the smoothness within the region takes precedence over the smoothness between regions. However, the anisotropic diffusion model has the property that the smooth region diffuses faster than the non-smooth region. feature, which causes the image to be prone to "block effect"
The fourth-order partial differential equation can overcome this challenge. This method uses the Laplacian operator in the energy function, and determines whether the image is a plane in its neighborhood by calculating whether the operator is zero. The partial differential equation will be divided The planar image of is approximated to the observation image to remove noise and preserve edges, but the disadvantage of this method is that it makes spots stand out
[0003] In practical applications, the diffusion function of the PM model cannot converge quickly for areas with obvious features, which results in smoothing of these details, and in addition, it cannot diffuse quickly in areas without obvious features
To solve this problem, researchers try to add local variance or local entropy to the diffusion coefficient to improve the protection of PM model for edge details, or combine shear wave transform (NSST) with anisotropic diffusion model. These methods Although there are certain effects, there is still room for improvement

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Adaptive edge preserving denoising method based on anisotropic diffusion model
  • Adaptive edge preserving denoising method based on anisotropic diffusion model
  • Adaptive edge preserving denoising method based on anisotropic diffusion model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] see figure 1 , the present embodiment provides an adaptive edge-preserving denoising method based on an anisotropic diffusion model, comprising the following steps:

[0040] step one

[0041] First read the original noise image to be denoised, and then perform Gaussian filtering on the noise image to obtain a preprocessed image;

[0042] step two

[0043] On the basis of the anisotropic diffusion model, Gaussian curvature is introduced into the adaptive image denoising algorithm model, edge detection is performed by image gradient, and then combined with the properties of Gaussian curvature and fractional differential operator, the score is established by the local variance of the image Order differential operator, add regularization term, construct adaptive edge-preserving denoising algorithm model:

[0044]

[0045] Among them, I 0 is the original image, I 1 for in I 0 On the image processed by Gaussian filtering, t is the diffusion scale, div and Denote th...

Embodiment 2

[0057] see figure 2 , to Gaussian noise σ=0.001 and multiplicative noise σ=0.005 "Lena", "photographer" and "village" grayscale images to carry out denoising experiment, use the method of the present invention and other several methods respectively in the experiment A comparison was made and the results are shown in Table 1 below. The parameters are set as follows: the number of iterations is 20, the threshold k 0 is 8, t=1 / 7, λ=0.01, β=1.5.

[0058] Table 1 Comparison table of grayscale images using different denoising methods under each index

[0059] method PSNR SSIM RMSE PM 30.1910 0.8247 0.0246 LEPM 27.7090 0.7866 0.0336 DEPS 30.2095 0.8248 0.0245 FDOGC 27.7117 0.7865 0.0464 proposed 31.1550 0.8472 0.0216

[0060] The gray scale images of "Lena", "Photographer" and "Village" are 512*512, 256*256, 700*700 respectively, Gaussian noise and multiplicative noise with variance of 0.001 and 0.005 are added to these ...

Embodiment 3

[0068] see Figure 4 , in order to verify the effectiveness of the method of the present invention in processing medical images, the Gaussian noise σ=0.001 and the multiplicative noise σ=0.005 noise-containing gray-scale MMR images were selected for experiments, and the image size was 256*256. In the experiment, the method of the present invention was compared with several other methods respectively, and the results are shown in Table 3 below.

[0069] Table 3 Comparison table of the results of nuclear magnetic images using different denoising methods under each index

[0070] method PSNR SSIM RMSE PM 27.8698 0.6919 0.0234 LEPM 27.5688 0.6765 0.0242 DEPS 27.7713 0.6919 0.0236 FDOGC 27.4868 0.7254 0.0244 proposed 27.9732 0.7320 0.0231

[0071] It can be seen from the image of the experimental results that, in the edge part, the method of the present invention is the best for edge preservation compared to the other fou...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a self-adaptive edge preserving denoising method based on an anisotropic diffusion model. The method comprises the following steps of preprocessing an original noise image, constructing a denoising algorithm model, performing iterative calculation on the original noise image and performing denoising processing on the original noise image. According to the method, the diffusion coefficient of the adaptive image denoising algorithm based on the combination of the fractional order differential operator and the Gaussian curvature is improved, bilateral filtering and local variance are added, the regularization item is introduced into the diffusion model, the image edge preserving effect is improved, the diffusion coefficient of the adaptive image denoising algorithm model is corrected, the denoising and edge maintaining effects are better, and the visual effect of the image is improved; the diffusion coefficient is adjusted by using the local variance so as to better control the diffusion speed; the image fidelity is improved by adding a regularization item, and an adaptive threshold is used, so that the medical image processing method is superior to a traditional image processing method in the aspect of processing a medical image besides a natural image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an adaptive edge-preserving denoising method based on an anisotropic diffusion model. Background technique [0002] In image processing, the anisotropic diffusion model has been paid attention to. The concept of diffusion mainly comes from multi-scale description. High-resolution images can be obtained by convolving the original image with Gaussian kernel to obtain low-resolution images. This description is theoretically can be regarded as an isotropic heat conduction equation. Perona and Malik first proposed the anisotropic diffusion model (PM model). The anisotropic diffusion model can adaptively select the diffusion direction during the diffusion process. This feature ensures that the smoothness within the region takes precedence over the smoothness between regions. However, the anisotropic diffusion model has the property that the smooth region diffuses faster than ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T2207/20004G06T2207/20028G06T5/70
Inventor 韩先君王雪李学俊王华彬周芃
Owner ANHUI UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products