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

Natural image denoising method based on non-local mean value of shearlet region

A non-local average and natural image technology, applied in the field of image processing, can solve the problems of natural image blurring and scratches

Active Publication Date: 2012-02-08
XIDIAN UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies in the above-mentioned prior art, and propose a natural image denoising method based on non-local means in the shearlet domain, so as to overcome the visual blurring of the natural image in the case of high noise in the existing method. The problem of severe scratch phenomenon, improve the denoising effect

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
  • Natural image denoising method based on non-local mean value of shearlet region
  • Natural image denoising method based on non-local mean value of shearlet region
  • Natural image denoising method based on non-local mean value of shearlet region

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021] refer to figure 1 , the implementation steps of the present invention are as follows:

[0022] Step 1. Select a test image and add Gaussian white noise with a standard deviation of 50 to it.

[0023] Step 2: Decompose the test image with Gaussian white noise with a standard deviation of 50 into Laplacian pyramid, decompose the test image into three layers, and apply the shearlet basis function generated by the second layer and the third layer respectively The filter bank performs directional filtering, that is, the number of filters in the shearlet filter bank is specified as four, and four sets of shearlet coefficients are obtained respectively; perform step 5 for the first layer.

[0024] The shearlet basis function described in step 2, the specific formula is as follows:

[0025]

[0026] in f ( ω ) = ( 1 / e ...

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 natural image denoising method based on a non-local mean value of a shearlet region, which mainly solves the problem that the traditional non-local mean value method has poor denoising effect of a natural image corroded by high noise. The method comprises the following implementation steps of: inputting a test image, and adding gaussian white noise with the noise standard deviation of 50; decomposing the image into three layers by utilizing a Laplacian pyramid method, wherein denoising treatment is carried out on the first layer by using a non-local mean value method, the second layer and the third layer are respectively decomposed into four groups of shearlet coefficients by using a shearlet directional filter group firstly, then estimation of a beta value is carried out on each group of shearlet coefficients, and then the denoising treatment of the non-local mean value method under a general Gauss model is carried out on each group of shearlet coefficients;and reconstructing a denoising result to obtain a final denoising result. The invention has the advantages of favorable denoising effect for the natural image corroded by high noise, can restore the original characteristics of the image and be used for variation detection and pretreatment of the image when an object is identified.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the denoising of natural images under high noise conditions, and can be used for forest resource investigation, land use, coverage change research, environmental disaster assessment, urban planning, national defense military situation monitoring, medical images and astronomical images, etc. A preprocessing technique often used in domain image processing. Background technique [0002] The rapid development of computer science and technology has had a huge impact on the field of digital image processing. Image denoising is an important branch in the field of image processing. The most basic method of processing is to estimate the noise image as: v(i)=u(i )+n(i), v(i) represents the noise image, u(i) represents the original image, and n(i) represents the noise, the purpose of image processing is to remove n(i), and estimate u( i), Image denoising is nothing more than the two...

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 张小华焦李成张强王爽王然侯彪钟桦尚荣华
Owner XIDIAN UNIV
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