Blind camera shake deblurring method based on L0 sparse prior

A sparse prior, camera shake technology, applied in image data processing, instruments, calculations, etc., can solve problems such as the inability to guarantee the optimality of the solution, lack of optimization theoretical support, etc., and achieve high-quality deblurring.

Active Publication Date: 2013-11-27
NANJING UNIV OF POSTS & TELECOMM
View PDF6 Cites 23 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current maximum a posteriori estimation method often uses the smoothing filter and the impact filter to predict the edge information of the image, and then uses the predicted edge image to estimate the blur kernel. After repeated iterations of these two processes, the blur kernel is finally estimated. Strict optimization theory support, so the (local) optimality of the solution cannot be guaranteed

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
  • Blind camera shake deblurring method based on L0 sparse prior
  • Blind camera shake deblurring method based on L0 sparse prior
  • Blind camera shake deblurring method based on L0 sparse prior

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0093] (1) Using the first-order derivative operators in the horizontal and vertical directions▽ h =[1,-1;0,0],▽ v =[1,0;-1,0], use the MATLAB function conv2 to obtain the gradient image of the camera shake blurred image y y d = ▿ d ⊗ y , d ∈ Λ , Λ = { h , v } :

[0094] the y h =conv2(y,▽ h ,'valid');

[0095] the y v =conv2(y,▽ v ,'valid');

[0096] (2) Let the size of the fuzzy kernel k to be estimated be Z×Z, in order to improve the convergence of the fuzzy kernel estimation method, a multi-scale implementation method is used to iteratively estimate the fuzzy kernel;

[0097] (3) Let the initial blur kernel k (1) =[0,0,0;1,1,1;0,0,0]3 (dimensions 3×3), and use the following MATLAB code to determine the total scale

[0098] The numb...

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 blind camera shake deblurring method based on the L0 sparse prior, and belongs to the technical field of digital image processing. The blind camera shake deblurring method is a method for deblurring blurred images caused by camera shaking, and various space-unchanged camera shaking blurred kernels, namely the point spread functions, can be estimated. The blind camera shake deblurring method solves the problem that a current variational bayes estimation method is high in computing complexity and solves the problem that a current maximum posteriori estimation method lacks strict optimization theory supports. The blind camera shake deblurring method comprises the steps of firstly, introducing remarkable edge sparse prior based on the L0 norm, and using the iterative hard threshold compressed method to achieve recessive automatic prediction of remarkable edge characteristics, secondly, introducing camera shake blurred kernel sparse prior, and using the iterative repeated weighted least square method to achieve rapid estimation of the blurred kernels, and finally, using the image non-blind deblurring method based on super-Laplacian prior to obtain a high-quality deblurred image. The flow diagram of the blind camera shake deblurring method is shown in the figure 1.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to the field of deblurring blurred images caused by camera shaking. Background technique [0002] Blind camera shake deblurring refers to the removal or reduction of image motion blur that occurs during the process of shooting digital images with a camera. It is a very important and challenging research content in digital image processing in recent years. Its core is to estimate the corresponding camera shake The blur kernel (point spread function). Currently, researchers have proposed a number of different methods. [0003] The most classic blind camera shake deblurring method is the maximum likelihood estimation method, which has been integrated into the image processing toolbox of the numerical calculation software MATLAB. High signal-to-noise ratio, and requires a small size of the blur kernel. Another disadvantage of this method is that the detailed informat...

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): G06T5/00G06T7/20
Inventor 邵文泽
Owner NANJING UNIV OF POSTS & TELECOMM
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