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

Four-order partial differential equation image denoising method based on mathematical morphology

A technology of partial differential equations and mathematical forms, applied in the field of image processing, can solve problems such as blurred edge details and achieve good visual effects

Active Publication Date: 2016-11-16
七腾机器人有限公司
View PDF2 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the YK model tends to over-smooth the high-frequency components of the processed image, blur edge details, and produce isolated impulse noise, that is, point effects.

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
  • Four-order partial differential equation image denoising method based on mathematical morphology
  • Four-order partial differential equation image denoising method based on mathematical morphology
  • Four-order partial differential equation image denoising method based on mathematical morphology

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings; it should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.

[0029] A fourth-order partial differential equation image denoising method based on mathematical morphology, comprising the following steps: S1 uses mathematical morphology to detect the edge of the noise image; S2 calculates the gradient magnitude of the noise image; S3 according to the mathematical morphology in step S1 The partial differential equation is established by the learned gradient operator and the gradient magnitude in step S2, and S4 adopts an iterative method to solve the partial differential equation to obtain a denoised image.

[0030] Mathematical morphology is a discipline based on strict mathematical theory. Its basic idea is to use certain structural...

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 four-order partial differential equation image denoising method based on mathematical morphology. The method comprises the following steps: S1) adopting a mathematical morphology method for detecting a noise image edge; S2) calculating a gradient magnitude of the noise image; S3) establishing a partial differential equation according to the gradient operator of the mathematical morphology in S1) and the gradient magnitude in S2); S4) adopting an iteration method for solving the differential equation and acquiring a denoised image. According to the invention, image denoising is performed through the four-order partial differential equation based on mathematical morphology, the processed image edge is better kept and the visual effect is better.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a fourth-order partial differential equation image denoising method based on mathematical form. Background technique [0002] In order to obtain good image quality, image denoising has become a basic task of image processing, and image denoising methods based on partial differential equations (PDEs) have been widely used in image processing in recent years. [0003] Perona and Malik (PM) proposed a classic anisotropic diffusion model in 1990, which controls the diffusion degree of the image through the diffusion function of the image gradient value, and has a good denoising effect. In addition to the PM model, other models based on partial differential equations such as the TV model, their variational nature is second-order partial differential. Although the second-order model has a good effect on image denoising, the processed image will have obvious block effects, w...

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): G06T5/00G06T7/00
CPCG06T2207/20192G06T5/70
Inventor 仲元红张顺欧翔周瑶雷绮仑张钊源杨萍李瑾
Owner 七腾机器人有限公司
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com