Check patentability & draft patents in minutes with Patsnap Eureka AI!

Image noise intensity estimation method for Gaussian noise

A technology of image noise and Gaussian noise, which is applied in the field of digital image processing, can solve the problem of distinguishing noise from the edge texture of the image, and achieve the effect of reducing the impact

Active Publication Date: 2017-10-03
JIANGSU OPEN UNIV
View PDF9 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But in fact, the structure of the edge texture of the image is ever-changing. Even with a suitable filter, there is no way to completely distinguish the noise from the edge texture of the image.

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
  • Image noise intensity estimation method for Gaussian noise
  • Image noise intensity estimation method for Gaussian noise
  • Image noise intensity estimation method for Gaussian noise

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047]The invention proposes an image noise intensity estimation method of Gaussian noise, and the difference value in the difference value histogram contains both noise and edge texture. Is it possible to process the difference distribution to make it closer to the difference distribution of the noise? Regardless of the structure of the image, the general image signal is regular (sparse feature). The difference distribution of image noise is different from that of edge texture. The difference distribution of noise is close to Gaussian distribution, while the difference distribution of edge texture is generally irregular. We can use the difference between noise and edge texture difference distribution to process the difference distribution to make it closer to the noise distribution. Then calculate the variance of the noise.

[0048] The following is based on Figure 1 to Figure 5 The specific embodiment of the present invention is further described:

[0049] see figure 1...

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 an image noise intensity estimation method for Gaussian noise. The image noise intensity estimation method comprises the steps of: subjecting an image to block processing to obtain a plurality of blocked images; filtering the blocked images; acquiring difference values between the original blocked images and the corresponding filtered blocked images, and recording the difference values in the form of a difference value histogram; cutting down a pixel number with large difference in the difference value histogram; calculating a mean square value of a positive number part and a mean square value of a negative number part of the difference values in the difference value histogram, extracting the minimum in the mean square value of the positive number part and the minimum in the mean square value of the negative number part, and regarding the minimums as noise intensity of the blocked images; and calculating a noise intensity value of each blocked image, and extracting the minimum from the noise intensity values of the blocked images to serve as noise intensity of the whole image. The image noise intensity estimation method for Gaussian noise can reduce the influence of the edge texture of the image, so as to further estimate the true noise.

Description

technical field [0001] The invention belongs to the field of digital image processing, in particular to an image noise intensity estimation method of Gaussian noise. Background technique [0002] Image denoising has a wide range of applications, and classic denoising algorithms such as BM3D and Non Local Means have very good denoising effects. However, general denoising algorithms need to know the noise level to denoise. Like BM3D, the denoising process of the Non local Means algorithm needs to know the variance of the noise and use the noise variance as a parameter in the calculation process. [0003] When denoising an image, we often need to estimate the noise level of the image, and we use the variance of the noise to describe the intensity of the noise. An inaccurate noise estimate can adversely affect the denoising process. If the estimate is too high, the denoising process will remove some textures that should not be removed. If the estimate is too low, the denoisin...

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): G06T7/00G06T5/00
CPCG06T7/0002G06T2207/20032G06T2207/20021G06T2207/30168G06T2207/20224G06T5/70
Inventor 邵文莎宋菲
Owner JIANGSU OPEN UNIV
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

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

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com