Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Denoising method of strong noise pollution image on basis of partial differential equation

A partial differential equation and strong noise technology, applied in the field of image processing, can solve the problems of unsatisfactory denoising effect, unsatisfactory denoising results, poor denoising results, etc., to achieve reduced interference, improved accuracy, and improved The effect of running speed

Inactive Publication Date: 2012-06-20
XIDIAN UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these denoising methods can better achieve the purpose of denoising, but for images with very low image details, the denoising results of these methods are not ideal.
[0004] In order to solve the problems of the above methods, denoising methods based on partial differential equations have become a hot issue in the field of image denoising research. Many scholars have classified, analyzed and improved the existing denoising methods from different perspectives, but for image Different feature areas, the effect of denoising is still not very ideal
Especially for some images with relatively high noise, the denoising results of these existing methods are poor and cannot meet the requirements

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
  • Denoising method of strong noise pollution image on basis of partial differential equation
  • Denoising method of strong noise pollution image on basis of partial differential equation
  • Denoising method of strong noise pollution image on basis of partial differential equation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0039] Step 1. The input size is M×N single noise image u 0 , use the wavelet soft thresholding method to image u 0 Perform pre-filtering to minimize the interference of strong noise on image texture details, so as to calculate edges and diffusion directions more accurately. The filtered result is recorded as u. In this example, both M and N are 512 or 256.

[0040] Step 2. Calculate the partial derivative of the image u in the x direction and the partial derivative in the y direction

[0041] Step 3. Based on the calculated partial derivatives and , use the gradient formula to calculate the gradient of the noise image u and gradient modulus

[0042] gradient gradient modulus

[0043] Among them, u x express , u y express

[0044] In the process of image denoising, it is required to retain as much detail information of the image itself as possible ...

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 denoising method of a strong noise pollution image on the basis of partial differential equation, mainly solving the problem of poor traditional denoising effect of the strong noise pollution image. The realization process comprises: (1) pretreating an input noise image u0, and recording the result as u; (2) calculating the partial derivative sum of the image u; (3) calculating the gradient module value of the image u; (4) according to the gradient and the gradient module value, building the partial differential equation; (5) calculating diffusion coefficients and psi in the partial differential equation; (6) utilizing the coefficients and psi, solving the partial differential equation to obtain a filter image; (7) calculating the peak signal to noise ratio PSNR of the filter image; and (8) repeating steps 2 to 7, when the PSNR value of the filter image output iteratively one time is smaller than that output iteratively in the previous time, stopping iteration, and outputting the previously iterated filter image. The invention has simple calculation and high operation speed, can better keep image texture details while smoothening strong noise and can be used for denoising a natural image with strong noise pollution.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image denoising method, and is suitable for noise removal of SAR images and natural images. Background technique [0002] Image denoising aims to process the noise-contaminated image through an algorithm to reduce the impact of noise on the original useful information and restore an image closer to the idealized image as much as possible. The preprocessing technology often used in image processing in the fields of change research, environmental disaster assessment, urban planning, national defense military monitoring, medical imaging and astronomical imaging has urgent needs and broad application prospects. Synthetic aperture radar SAR images and natural images both need denoising processing, and research on SAR image and natural image denoising processing technology has a very broad application prospect. [0003] In order to meet the urgent needs of image denoising applic...

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 Patents(China)
IPC IPC(8): G06T5/00
Inventor 张小华王爽王然焦李成张强侯彪于昕尚荣华
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products