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

Efficient Blind Image Deblurring Method Based on Edge Adaptation

An adaptive, image-based technology, applied in the field of image processing, can solve problems such as feasibility and complexity that are difficult to guarantee in theory and effect

Inactive Publication Date: 2016-07-06
XIDIAN UNIV
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In order to obtain a clear image, it is usually necessary to use some complicated methods to sharpen the edges of the image, but this method is not only complicated, but also difficult to guarantee its feasibility in theory and effect

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
  • Efficient Blind Image Deblurring Method Based on Edge Adaptation
  • Efficient Blind Image Deblurring Method Based on Edge Adaptation
  • Efficient Blind Image Deblurring Method Based on Edge Adaptation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] The present invention will be described in further detail below in conjunction with the accompanying drawings.

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

[0031] Step 1: Input the blurred image, and set the gradient domain image of the clear image to be solved.

[0032] Input a blurred image y, the blur kernel of the blurred image is k; let the clear image to be solved be x, and the gradient domain image of the clear image x to be solved be Dx.

[0033] Step 2: Initialize parameters:

[0034] Initialize the mean μ of the gradient domain image Dx of the image to be solved c is 0; initialize the number of times of deblurring f=0; set the initial solution of clear image x as blurred image y.

[0035] Step 3: Update the blur kernel.

[0036] 3a) Set the number of iterations L, initialize the iteration number l to 1, and initialize the fuzzy kernel before the iteration It is a matrix with all 1 values. The dimension...

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 an image blind deblurring method based on edge self-adaption. To solve the problems that as for an existing total variation deblurring algorithm, edges and details of images are easily blurred, a de-mean gradient total variation canonical model is built, weighting coefficients are calculated in an iterated mode by means of local variance self-adaption of gradients of the images, and the ability of the deblurring algorithm to restore the edges and the details of the images. The image blind deblurring method comprises the following steps that (1) a blurred image is input, solutions to a gradient-region clear image and a blurring kernel are obtained alternately, and the initial blurring kernel of the blurred image is obtained; (2) the initial blurring kernel is used for conducting primary non-blind deblurring on the blurred image, and an initial clear image is obtained; (3) clustering is conducted on the initial clear image, the mean value and the weighting coefficient in the de-mean canonical model are updated, and a solution to the blurring kernel is obtained again; (4) the new blurring kernel is used for conducting secondary non-blind deblurring so as to obtain a clear image. Experimental results show that the image blind deblurring method based on edge self-adaption has better deburring effect than the prior art and can be used for image restoration.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a blind image deblurring method, which can be used for image restoration. Background technique [0002] During the camera imaging process, due to camera shake, defocus, defocus, or target object movement, the photos taken by people are often blurred. Restoring a clear digital image from a single blurred image has important requirements in digital media entertainment, national defense and security. Usually, the information such as the motion parameters of the camera relative to the scene is unknown. In order to remove the blur of the image, it is necessary to estimate the blur kernel of the image and the clear digital image at the same time. Deblurring the image when the blur kernel is unknown is the blind image removal. Blurring, image blind deblurring is a relatively difficult image restoration problem. [0003] The image blind deblurring problem can be expressed as ...

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 董伟生吕雪银石光明
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