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

Image denoising method based on non-local means and multi-level directional images

A non-local mean and image noise reduction technology, applied in the field of image processing, to achieve accurate target and background information, wide application prospects, and ideal noise reduction effects

Active Publication Date: 2010-09-29
SHANGHAI UNIVERSITY OF ELECTRIC POWER +1
View PDF6 Cites 30 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the deficiencies in current image denoising methods, and proposes an image denoising method based on non-local mean value and multi-level directional image, which uses the improved non-local mean value algorithm and non-subsampling Contourlet transform (NSCT) Combined to denoise images to improve image 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
  • Image denoising method based on non-local means and multi-level directional images
  • Image denoising method based on non-local means and multi-level directional images
  • Image denoising method based on non-local means and multi-level directional images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] The image denoising method based on non-local mean and multi-level directional image first utilizes the similarity of the local structure of the image, and uses the non-local mean algorithm of small window in the spatial domain to preprocess the noised image to remove high-frequency noise, and uses principal component analysis The algorithm (PCA) maps the local window to a low-dimensional space to improve the speed of the algorithm. The preprocessed image is then subjected to multi-scale and multi-directional sparse decomposition by NSCT. In the NSCT transform domain, the Wiener filter is used to eliminate low-frequency noise by using the neighborhood statistical properties of the coefficients. And the noise-reduced image is obtained by NSCT inverse transformation to achieve the purpose of image noise reduction.

[0027] Assume that the observed noise image is I=f+n(1), where f is the original image, and n is the Gaussian white noise signal N(0, σ 2 ).

[0028] The s...

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 an image denoising method based on non-local means and multi-level directional images, comprising the steps of: firstly, carrying out preprocessing on an image with noise in an empty space by utilizing a non-local mean arithmetic of a small window and the similarity of the local structure of the image, and mapping the local window to a low-dimensional space to improve the speed of the arithmetic by utilizing principal component analysis (PCA); then carrying out multi-scale multi-direction sparse decomposition on the preprocessed image through NSCT (Non-subsampled Contourlet); eliminating low-frequency noise by adopting Wiener filtering in an NSCT transform domain by utilizing the neighborhood statistical property of the coefficient; and obtaining the denoised image through NSCT inverse transformation. The method improves the quality of the denoised image, provides more comprehensive and accurate targets and background information, and achieves relatively ideal denoising effect. The invention has wide application prospect in systems in the military field and the non-military field such as optical imaging, target detection, safety monitoring, and the like.

Description

technical field [0001] The invention relates to an image processing technology, in particular to an image noise reduction method based on non-local mean and multi-level directional images. Background technique [0002] Images are usually polluted by varying degrees of noise in the process of acquisition and transmission. Image noise has a great impact on image analysis, image compression, etc. It is necessary to perform noise reduction processing to obtain images with high signal-to-noise ratio and clear details. Therefore, Image denoising has been a very active research topic in the field of image processing. The existing image denoising work can be summarized into two categories: spatial domain denoising and frequency domain denoising methods. The more classic methods in spatial denoising include: Gaussian filter, median filter, Wiener filter, etc. Gaussian filter is isotropic and does not distinguish between edges and details, so this method is easy to cause blurring of ...

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): G06T5/00G06T5/10G06V10/30
CPCG06K9/40G06V10/30
Inventor 赵倩
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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