Recovery method of low-rank matrix reconstruction with random value impulse noise deletion image

A technique for impulse noise and image restoration, applied in the field of computer vision

Inactive Publication Date: 2016-09-21
TIANJIN UNIV
View PDF4 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The technical scheme adopted by the present invention is that low-rank matrix reconstruction has a random value impulse noise missing image recovery method, combines matrix reconstruction theory with sparse...

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
  • Recovery method of low-rank matrix reconstruction with random value impulse noise deletion image
  • Recovery method of low-rank matrix reconstruction with random value impulse noise deletion image
  • Recovery method of low-rank matrix reconstruction with random value impulse noise deletion image

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The dictionary learning model is introduced on the basis of the traditional matrix reconstruction model, so that the structurally missing low-rank matrix with random value impulse noise can be reconstructed to obtain the restored image, that is, the reconstructed image based on the low-rank matrix with random value A missing image restoration method based on impulse noise, thereby solving problems that cannot be dealt with by existing technologies. The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0048]1) Considering the image as a matrix, the original image can be represented by a matrix A. To solve the missing image restoration problem with random-valued impulse noise is to solve the following optimization equation:

[0049]

[0050] ||A|| * Indicates the kernel norm of matrix A. ||·|| 1 Indicates the one-norm of the matrix. Ω is the observation space, p Ω (·) is a projection operator, whi...

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 present invention belongs to the field of the computer vision, and the objective of the invention is to realize the recovery of the image with random value impulse noise structural deficiency. The method comprises: combining the matrix reconstruction theory and the sparse expression theory, leading into a dictionary learning model on the basis of the traditional matrix reconstruction model, and therefore solving the problems which cannot be solved in the prior art. The method comprises the following steps: 1) taking the image as a matrix, expressing an original image by using a matrix A, and solving the deletion image recovery problem with random value pulse noise to solve the following optimization equation; 2) training a dictionary [Phi]; 3) converting a sequence to another sequence for solution by using an alternative direction method (ADM), wherein a contraction operator is included; and performing iteration solution to obtain a final result according to the steps. The recovery method of low-rank matrix reconstruction with a random value impulse noise deletion image is mainly applied to the computer image processing.

Description

technical field [0001] The invention belongs to the field of computer vision. In particular, it concerns missing image restoration methods with random-valued impulse noise based on low-rank matrix reconstruction. Background technique [0002] The matrix reconstruction problem is mainly divided into matrix filling and matrix restoration, which has attracted much attention and has strong vitality since it was proposed. Especially in the era of big data, it has become a research hotspot in the fields of mathematics and computers. In recent years, there have been many research results on algorithms to solve the matrix reconstruction problem. Since the rank minimization problem of the matrix is ​​a non-convex optimization problem, the current algorithm mainly uses the iterative singular value decomposition method to approximate the solution of the original model. Such as SVT (singular value threshold) algorithm, APG (accelerated neighbor gradient) algorithm, ALM (augmented Lag...

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
IPC IPC(8): G06T5/00
CPCG06T5/001G06T5/002
Inventor 杨敬钰杨雪梦叶昕辰
Owner TIANJIN UNIV
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