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

Method for determining intensity of speckle noise in images

A technique of speckle noise, noise intensity, applied in the field of image processing

Inactive Publication Date: 2010-08-18
SHAANXI NORMAL UNIV
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of current methods for determining noise intensity, and provide a method for quickly and accurately determining the intensity of speckle noise in an 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
  • Method for determining intensity of speckle noise in images
  • Method for determining intensity of speckle noise in images
  • Method for determining intensity of speckle noise in images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] Taking the speckle noise intensity of the simulated image (containing speckle noise intensity as 0.02) as an example, the method steps are as follows:

[0043] 1. Select an area with relatively uniform gray scale

[0044] Select a relatively uniform gray-scale area 1 in the simulated image with speckle noise intensity of 0.02, and the selected gray-scale area 1 is the area inside the rectangular box, see figure 2 .

[0045] 2. Calculate the gray mean value of the pixels in the selected area and the Gauss-Hermitian moments of different orders of pixels in the area

[0046] The above-mentioned average gray value is the average gray value of each pixel in the relatively uniform gray area, and the average gray value of one pixel in the gray area is 191.

[0047] The Gauss Hermitian moments of different orders of each pixel in the gray area 1 are calculated according to the following formula:

[0048] M p , ...

Embodiment 2

[0072] Taking the determination of the speckle noise intensity of a visible light image "coin" (with a speckle noise intensity of 0.1) as an example, the method steps are as follows:

[0073] In step 1 of selecting a relatively uniform gray scale area, select a relatively uniform gray scale area 2 in the visible light image "coin" with a speckle noise intensity of 0.1, and the selected gray scale area 2 is the area inside the rectangular box, see image 3 .

[0074] In step 2 of calculating the gray mean value of the pixels in the selected area and the different order Gauss-Hermitian moments of the pixels in the area, the gray mean value of the pixels in the gray area 2 is 226, and each pixel in the gray area 2 The calculation formulas used for different orders of Gauss-Hermitian moments are the same as in Embodiment 1, and the values ​​of t, v, and σ in formulas (1) to (4) are the same as in Embodiment 1.

[0075] Step 3 of constructing feature vectors is the same as that of...

Embodiment 3

[0080] Taking the determination of the speckle noise intensity of a synthetic aperture radar coastline image as an example, the method steps are as follows:

[0081] In the step 1 of selecting a relatively uniform gray scale area, a relatively uniform gray scale area 3 is selected in the SAR coastline image, and the selected gray scale area 3 is the area within a rectangular box, see Figure 4 .

[0082] In step 2 of calculating the gray mean value of the pixels in the selected area and the different order Gauss-Hermitian moments of the pixels in the area, the gray mean value of the 3 pixels in the gray area is 25, and the difference between the 3 pixels in the gray area The calculation formula used for the first-order Gauss-Hermitian moment is the same as that in Embodiment 1, and the values ​​of t, v, and σ in formulas (1)-(4) are the same as those in Embodiment 1.

[0083] Step 3 of constructing feature vectors is the same as that of Embodiment 1.

[0084] In step 4 of ca...

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 method for determining the intensity of speckle noise in images, which comprises the six steps of: selecting an area of which the gray scale is relatively uniform; calculating the grayscale mean value of pixels in the relatively-uniform area and different orders of Gaussian-Hermite moments of all pixels; constructing feature vectors; calculating and detecting noise intensity characteristic values of the images; converting the noise intensity characteristic values; and determining intensity values of speckle noise in the images. In the method, on the basis of a multiplicative noise module, the intensity of the noise is determined under the condition of no any priori knowledge, and thus the method has the characteristics of high precision, high speed and strong practical applicability and generality, and can be used for determining the intensity of the noise of the images containing the speckle noise, such as visible light images with grayscale uniform areas, synthetic aperture radar images, medical ultrasound images and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for determining the intensity of speckle noise in an image in combination with a Gaussian-Hermite moment (Gaussian-Hermite)-based noise analysis model. Background technique [0002] The process of image acquisition and transmission makes most digital images contain varying degrees of noise, which not only affects the visual effect of the image, but also hinders subsequent processing such as target detection, feature extraction, and parameter measurement, directly affecting the quality of image interpretation. [0003] According to the superposition method of noise and image, image noise includes two types: multiplicative noise and additive noise. The current research focuses on Gaussian noise in additive noise. The existing representative results of noise parameter estimation include: In 1990, Peter Meer et al. took Gaussian noise with zero mean valu...

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): G06T7/00
Inventor 马苗丁生荣张艳宁郭敏
Owner SHAANXI NORMAL 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