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

Total-variation (TV) regularized image blind restoration method based on Split Bregman iteration

A blind restoration and total variation technology, applied in the field of image processing, can solve problems such as complex blur types or poor restoration effect of complex images, and achieve the effect of stable and fast solution process.

Inactive Publication Date: 2012-10-31
CHONGQING UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The present invention is a total variation regularized blind restoration method based on Split Bregman iteration, which overcomes the shortcomings of the traditional total variation blind restoration method for complex fuzzy types or complex image restoration, and makes the restoration method have good robustness to noise. Rod

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
  • Total-variation (TV) regularized image blind restoration method based on Split Bregman iteration
  • Total-variation (TV) regularized image blind restoration method based on Split Bregman iteration
  • Total-variation (TV) regularized image blind restoration method based on Split Bregman iteration

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021] figure 1 It is the basic frame diagram of the method of the present invention, and this method mainly is made of following four core steps:

[0022] Step 1: Define the TV regularized blind restoration minimization cost function, and use the operator replacement method to replace the gradient operator in the blind deconvolution model: , , transforming the minimization problem into a constrained optimization problem

[0023] In image restoration, the degradation of most images can be regarded as a linear process, which can be expressed by the following formula:

[0024]

[0025] in K is a linear operator representing the point spread function (PSF) that blurs the image, u Indicates the requested original clear image, n is an additive noise with a variance of 1 and a mean of 0, f is a known degraded image.

[0026] The task of image restoration is based on known degraded images f get a clear image u . In blind restoration, the PSF responsible for blurring th...

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 total-variation (TV) regularized image blind restoration method based on Split Bregman iteration, belonging to the field of image processing technology. The method comprises the following key steps: constructing a TV regularized blind restoration cost function based on prior information and regularization features of images; converting a minimization problem into a new constraint solving problem by using an operator splitting technique; converting the constraint solving problem into a split cost function by using a method added with penalty terms; and proposing an extended Split Bregman iteration solving frame to solve the split cost function. Experiments show that the method provided by the invention can effectively and rapidly restore the image, and can restore a plurality of blur types. Additionally, the method can overcome the disadvantage of poor restoration effect for complicated blur type or complicated image of the conventional TV regularized blind restoration method, and the method has good noise robustness.

Description

technical field [0001] The invention belongs to the technical field of image processing. Background technique [0002] Image is one of the most important sources of information for people. However, in the process of image acquisition and transmission, due to the interference of various factors, the image will be degraded and degraded. The degradation of the image will cause a large amount of real information to be lost, which will not only reduce the scientific value of the image, but also bring huge economic losses. Therefore, we need to use image restoration technology to restore the original appearance from the degraded image. At present, image restoration technology has been applied to many fields of science and technology, such as astronomical observation, medical imaging, multimedia, criminal investigation, etc. Many image restoration methods require more prior information, or have disadvantages such as poor effect and high algorithm complexity. So far, developing a...

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 李伟红李权利龚卫国唐述李正浩杜兴
Owner CHONGQING 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