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

Noise variance estimating method based on broad sense autoregression heteroscedasticity model

A noise variance estimation and autoregressive technology, applied in the field of image processing, to achieve the effect of improving accuracy

Inactive Publication Date: 2015-05-13
张振军
View PDF5 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the defects of existing noise-containing image noise variance estimation techniques, and propose a noise variance estimation method based on non-subsampling contourlet transform and generalized autoregressive heteroscedastic model, which can improve the accuracy of noise variance estimation It is suitable for degraded images with various noise levels, and provides support for subsequent image processing such as denoising, restoration, and feature extraction.

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
  • Noise variance estimating method based on broad sense autoregression heteroscedasticity model
  • Noise variance estimating method based on broad sense autoregression heteroscedasticity model
  • Noise variance estimating method based on broad sense autoregression heteroscedasticity model

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0062] A noise variance estimation method based on the generalized autoregressive heteroscedastic model of the present invention, such as Figure 1 to Figure 3 shown, including the following steps:

[0063] Step 1, read the noisy image g contaminated by noise x,y .

[0064] Read a noisy image contaminated by noise g x,y = f x,y +n x,y , where f x,y Indicates a clear image, n x,y Represents a noisy image, the noisy image g x,y , clear image f x,y , noise image n x,y The size is N×M, n x,y Obedience mean is 0, variance is σ 2 The normal distribution of n x,y ~N(0,σ 2 ), the subscripts x and y represent the row coordinates and column coordinates of the image respectively, 1≤x≤N, 1≤y≤M.

[0065] Step 2, for the noisy image g polluted by noise x,y Do a non-subsampled contourlet transform, i.e. NSCT.

[0066] For noise-contaminated images g x,y Do L-level non-sub-sampling contourlet transformation (the best value of L in this implementation example is 3 or 4), where ...

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

A noise variance estimating method based on a broad sense autoregression heteroscedasticity model includes the following steps: 1 reading an image with noise and polluted by the noise; 2 conducting non-sub-sampling contourlet transformation; 3 conducting de-mean filtering processing on each high frequency sub-band coefficient matrix in the step 2; 4 converting the high frequency sub-band coefficient matrix subjected to de-mean filtering processing into one-dimensional sequence data; 5 building an autoregression model on one-dimensional sequence data to obtain a residual sequence of the data; 6 building a statistical model for the residual sequence; 7 adopting a maximum likelihood estimation method to calculate the parameter of the statistical model according to the residual sequence obtain in the step 5 and the statistical model obtained in the step 6; 8 acquiring the variance of the noise in the image with the noise. By means of the method, noise variance estimation accuracy can be improved, and the method is applicable to degraded images of various noise levels, and provides support for follow-up image processing including noise reduction, restoration, characteristic extraction and the like.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a noise variance estimation method based on a generalized autoregressive heteroscedastic model. Background technique [0002] Due to the influence of various factors such as imaging environment and transmission channel, the obtained digital images are often polluted by noise. Noise not only reduces the quality and visual effect of the image, but also affects various subsequent image processing and analysis processes. For example, the selection of regularization parameters in image restoration, the value of balance factor in sparse representation, and the determination of the optimal number of quantization in image compression all depend on the prior knowledge of noise variance, and the accuracy of noise variance estimation will significantly affect Affects the performance of image denoising, restoration, representation, compression, segmentation, feature extraction, obj...

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
Inventor 张振军
Owner 张振军
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