An Image Denoising Method Based on Analytical Sparse Representation

A sparse representation and image technology, applied in image enhancement, image data processing, instruments, etc., to achieve the effect of removing noise, accurate target and background information, and improving performance

Inactive Publication Date: 2016-08-17
NANCHANG UNIV
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of these methods use a greedy tracking algorithm with a large amount of calculation to estimate the source signal, so these methods are not optimal.

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
  • An Image Denoising Method Based on Analytical Sparse Representation
  • An Image Denoising Method Based on Analytical Sparse Representation
  • An Image Denoising Method Based on Analytical Sparse Representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] This image denoising method based on analytic sparse representation first uses the noisy image to learn the analytic dictionary through the subset tracking algorithm, then uses the Bregman distance as the objective function, uses the weighted split Bregman algorithm to estimate the source signal, and obtains the final denoising image to achieve the purpose of image denoising.

[0040] The specific steps are:

[0041] ① Extract K from the overlap of noisy images The size of the image sub-blocks, the sub-blocks are arranged in columns to obtain the training data matrix Y∈R n×K .

[0042] ② Use the subset tracking algorithm to train Y to obtain the analytical dictionary Ω∈R p×n . In the subset pursuit algorithm, the number of iterations is set to N, and the common sparsity is set to l.

[0043] ③Using the weighted split Bregman algorithm to estimate the source signal, the optimization function of the algorithm is:

[0044] min ...

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

An image denoising method based on analytic sparse representation. Firstly, the analytic dictionary is obtained by using the noisy image through subset tracking algorithm learning, and then the Bregman distance is used as the objective function, and the weighted split Bregman algorithm is used to estimate the source signal to obtain the final denoising noise image to achieve the purpose of image denoising. The image denoising method provided by the present invention can improve image quality, provide more accurate target and background information, and achieve a more ideal denoising effect. Wide range of applications.

Description

technical field [0001] The invention relates to an image denoising method based on parsing sparse representation. Background technique [0002] Usually, the image will be polluted by noise in the process of acquisition and transmission, and it is necessary to perform denoising processing for subsequent processing. The purpose of denoising is to filter out noise as much as possible, while retaining the information of the source image to the greatest extent, so as to improve the quality of the image. At present, image denoising processing methods can generally be divided into spatial domain processing and transform domain processing. Classical spatial domain denoising processes include mean filtering, median filtering, and Wiener filtering. The basic idea of ​​transform domain denoising is to transform the noisy image from the spatial domain to the transform domain, process the coefficients in the transform domain, and then inversely transform the image to obtain the denoise...

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 Patents(China)
IPC IPC(8): G06T5/00
Inventor 张烨余腾龙龚黎华张文全
Owner NANCHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap