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

Image noise level estimation method based on principal component analysis

A technology of principal component analysis and image noise, applied in image enhancement, image analysis, image data processing, etc.

Inactive Publication Date: 2016-06-15
ZHEJIANG UNIVERSITY OF MEDIA AND COMMUNICATIONS
View PDF2 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This type of method usually assumes that the difference image is noise, but this assumption is not always true, especially when the image has a relatively complex structure or good details.

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 level estimation method based on principal component analysis
  • Image noise level estimation method based on principal component analysis
  • Image noise level estimation method based on principal component analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0035] like figure 1 As shown, an image noise level estimation method based on principal component analysis includes the following steps:

[0036] (1) Divide the input noise image into image blocks with a size of 9×9. The specific method is: set the size of the window to 9×9, and then move the window from left to right and from top to bottom to scan the noise image. The part of the image within the window after each movement is an image block, and the moving step is 1 pixel.

[0037] (2) Write all image blocks in the form of column vectors, denoted as y i ,y i is the i-th image block, and then combine all the image blocks into a large data matrix, denoted as Y.

[0038] (3) According to the following formula, calculate the covariance matrix Σ corresponding to the data matrix Y y :

[0039] Σ y = 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 level estimation method based on principal component analysis. The method comprises: step 1, dividing a raw image into a plurality of image blocks with the same size; step2, turning the selected image blocks into an image data matrix, and calculating a covariance matrix of the image data matrix; step 3, conducting eigen decomposition of the covariance matrix, setting an obtained minimal eigenvalue as an estimate of the noise level, and using the estimate of the noise level to calculate a threshold; step 4, calculating a gradient matrix corresponding to each image block and the trace of a covariance matrix corresponding to the gradient matrix; and step 5, picking the image block with the trace not greater than the threshold, and repeating the step 2 to 4 until the estimates of the noise level of two continuous calculations are the same or the iteration is repeated for a preset number of times. The method uses principal component analysis to find a smooth block in the image more accurately. The method is precise and robust and is applicable to large-scale noise levels and various scenes.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to an image noise level estimation method based on principal component analysis. Background technique [0002] Noise level is an important parameter in many image processing programs, such as image denoising, image segmentation, etc. The performance of most current denoising algorithms relies heavily on the estimation of the noise level, and the solution basically assumes that the noise level is known, but it is obviously not suitable for real scenes. [0003] The most common noise model is additive Gaussian white noise. Taking this as an example, the goal of the noise level estimation algorithm is to estimate the standard deviation σ of the unknown Gaussian noise given a single noisy image. n . Many algorithms have been proposed for this problem. Generally, these methods can be divided into filtering-based methods, image block-based methods and statistical-base...

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
CPCG06T7/0002G06T2207/20021G06T2207/30168
Inventor 张根源
Owner ZHEJIANG UNIVERSITY OF MEDIA AND COMMUNICATIONS
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