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

Wavelet and small curve fuzzy self-adapting conjoined image denoising method

A fuzzy self-adaptive and ditty technology, applied in the field of image processing, to achieve the effect of solving block effect and good denoising quality

Inactive Publication Date: 2008-10-29
安冉 +1
View PDF0 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It can more flexibly integrate the advantages of wavelet transform and small curvature transform in processing flat areas and edge areas of images respectively, so as to more perfectly solve the conflict between noise removal and edge preservation in the denoising process, and further improve the performance of denoising images. quality

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
  • Wavelet and small curve fuzzy self-adapting conjoined image denoising method
  • Wavelet and small curve fuzzy self-adapting conjoined image denoising method
  • Wavelet and small curve fuzzy self-adapting conjoined image denoising method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] Concrete implementation steps of the present invention are as follows:

[0026] 1. Chunk

[0027] Add Gaussian white noise (Gaussian white noise is the most commonly used noise model) to the noise-free optical image Lena of 256×256 with mean value 0 and variance 0.02 to generate the noised image I and block it. The block size b is set to is 16×16.

[0028] 2. Calculate the flatness membership function value of the sub-block

[0029] σ 1 and σ 2 Take the values ​​1 and 1.5 respectively.

[0030] 3. The results of fusion sub-blocks using DWT method and DCT method to denoise

[0031] In the realization of DWT method, the wavelet function coif2 is selected to decompose I into three layers, and the soft threshold method is used to process DWT coefficients; in the realization of DCT method, the realization of discrete ditto transform is carried out by dividing the image into three layers of subbands, and the minimum block size is taken as is 16, and the DCT coefficients...

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 relates to a new method which combines the fuzzy adapting of wavelet transform and curvelet transform in the image noise removal. The noise removal is one of important research programs in the image processing; however, the existing noise removal method can not completely solve the conflict between the noise removal and the edge preserving. The invention aims at providing an image noise removal method with the combination of the wavelet and curvelet fuzzy adapting on the basis of the defect of the prior art. The method of the invention establishes a flatness membership function of a sub-block to fuzzy express the edge information content in the sub-block and takes the membership function as the weight factor to carry out the data fusion to each sub-block by adopting the results from the noise removal with the wavelet transform and the curvelet transform. The method of the invention has the advantages that the data fusion substitutes the compulsory smoothing processing of the adapting combination method to solve the problem of blocking effect more thoroughly and retain more edge details; the advantages of the noise removal with the wavelet and the curvelet are flexibly integrated by the fuzzy data fusion so as to further improve the quality of noise removal.

Description

technical field [0001] The invention relates to a novel image denoising method combining wavelet transform and Curvelet transform, which belongs to the field of image processing. Background technique [0002] Images are inevitably disturbed by noise during the process of generation and transmission. The existence of noise leads to the reduction of image quality and the increase of the difficulty of image analysis work such as target recognition in the later stage. Therefore, how to effectively remove image noise has always been one of the important research topics in the field of image processing. [0003] The classic image denoising methods include spatial filtering method, frequency domain filtering method and the more commonly used denoising method based on discrete wavelet transform (DWT method for short) in recent years. Although they have achieved good denoising effects, they still cannot completely solve the conflict between noise removal and edge preservation in th...

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
IPC IPC(8): H04N5/14H04N5/21
Inventor 安冉王楠
Owner 安冉
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