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

Low-order decomposition method for blind deblurring of images

A blind deblurring and low-rank decomposition technology, applied in the field of image processing, can solve problems such as image sharpening, distortion, and inability to effectively use all image information, and achieve the removal of ringing effects, restoration of blurred images, and good deblurring effects Effect

Active Publication Date: 2015-04-08
XIDIAN UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the image obtained by this method is too sharp, resulting in distortion, and this method is only the last image in the output iteration, which cannot effectively use all the information of the image, so the restoration result lacks high-frequency 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
  • Low-order decomposition method for blind deblurring of images
  • Low-order decomposition method for blind deblurring of images
  • Low-order decomposition method for blind deblurring of images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0031] Step 1, pre-restore the blurred image b to obtain an updated iterative image and the updated blur kernel

[0032] 1a) Set the iteration flag to i=1, the iteration maximum value to imax=45, and set the iteration image y i initial value of y 0 is the blurred image b, the blur kernel k i The initial value of k 0 is a Gaussian impulse function;

[0033] 1b) Calculate the iteration image y according to the following formula i :

[0034] y i = IFFT [ ( 1 - α ) P i - 1 + α XW * i - ...

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 low-order decomposition method for blind deblurring of images, and mainly aims to solve the problem that in the prior art, the image edge and high-frequency details cannot be recovered favorably during blind deblurring of images. The implementation process comprises the following steps of: (1) pre-restoring a blurred image b by using a frequency domain iteration method to obtain an iteration image and a blurring kernel i=1,2,3...45; (2) normalizing each image in the iteration image i=1,2,3...45 to obtain a normalized iteration image i=1,2,3...45; (3) pulling each image of the normalized iteration image i=1,2,3...45 into a column, and forming high-dimensional data M in the order of i=1,2,3...45; (4) calculating a low-order matrix L of the high-dimensional data M; (5) restoring each column in the low-order matrix L into an image to obtain a low-order image ri, i=1,2,3...45; and (6) carrying out mean processing on the low-order image ri, i=1,2,3...45 to obtain a final sharp image F. By adopting the method, the iterated image information can be fully utilized, the ring effect is removed, and sharp images with abundant details can be restored. The method can be used for blind deblurring of various blurred images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a blind deblurring method for blurred images, which can be used for deblurring blurred images of various unknown blur types. Background technique [0002] The purpose of image deblurring is to re-estimate the original image from the observed blurred image. Image deblurring is divided into two categories: non-blind image deblurring and blind image deblurring. If the degradation process of the image is known, that is, the blur kernel is known, then this type of image deblurring problem is called image non-blind deblurring. This type of problem has been studied very well, and many existing technologies can get very clear solutions; If the blur kernel of the image is unknown, the problem of this type of image deblurring is called image blind deblurring. Because the empirical knowledge that can be used in such problems is relatively small, it is more difficult to blindly de...

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 王爽焦李成李源梁冲季佩媛郑喆坤白静
Owner XIDIAN 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