Multiplicative noise removal method for image

A multiplicative noise and image technology, applied in the field of digital image processing, can solve problems such as poor adaptability and loss of details, and achieve the effect of reducing workload and strengthening removal

Inactive Publication Date: 2015-05-27
GUILIN UNIV OF ELECTRONIC TECH
View PDF3 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The technical problem to be solved by the present invention is that the existing image multiplicative noise removal methods have problems such as loss of details and poor adaptability that affect the visual effect of the image to varying degrees, and a method for image multiplicative noise removal is provided

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
  • Multiplicative noise removal method for image
  • Multiplicative noise removal method for image
  • Multiplicative noise removal method for image

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] An image multiplicative noise removal method, such as figure 1 shown, including the following steps:

[0030] Step 1: Perform normalized preprocessing on the original image with multiplicative noise, that is, the noisy image, that is, perform logarithmic transformation on the noisy image.

[0031] In order to simplify the calculation amount, the image is normalized, and the multiplicative noise is converted into additive noise through logarithmic transformation, that is, the noise is removed in the logarithmic domain. The logarithmic transformation model is Where y is the observed image, x is the denoised image to be obtained, v is the multiplicative noise obeying the Gamma distribution, f is the observed image after logarithmic transformation, and z is the denoised image after logarithmic transformation The ideal image of w is the logarithmically transformed noise.

[0032] Step 2: Divide the image in the logarithmic domain into blocks, and then perform non-local si...

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 a multiplicative noise removal method for an image. According to the method, calculus of variations and low rank constraint condition are embedded on the basis of a non-local learned dictionary based on sparse representation, non-local similar patches are subjected to weight matching, a sparse coding is solved with an iterated function, and a soft threshold algorithm is applied to low rank solution. The multiplicative noise removal method has the advantages that very good noise removal effect and high peak signal to noise ratio are realized, edge information and textural features of the image are reserved well to be more close to an original image visually, and the similarity is improved greatly.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to an image multiplicative noise removal method based on non-local similarity sparse representation and low-rank constraints. Background technique [0002] Image denoising is one of the most basic problems in image processing, and its purpose is to restore the original image from the noisy image. Since multiplicative noise widely exists in imaging fields such as nuclear magnetic resonance, remote sensing, and synthetic aperture radar, it reduces image quality and seriously affects image segmentation and target detection. Therefore, how to effectively remove multiplicative noise is a coherent Top questions in imaging. [0003] At present, in the multiplicative noise removal algorithm, the more common methods are variation-based and sparse representation-based methods. Variation-based methods are iteratively denoised using a variational model that includes a loyalty...

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): G06T5/00
Inventor 陈利霞朱平芳王学文莫建文袁华张彤首照宇欧阳宁
Owner GUILIN UNIV OF ELECTRONIC TECH
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