Unlock instant, AI-driven research and patent intelligence for your innovation.

Image restoration method based on low-rank tensor ring decomposition and high-order structuralization

A repair method and structured technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as image data distortion, and achieve the effect of taking into account the efficiency

Pending Publication Date: 2020-11-17
ZHEJIANG UNIV OF TECH
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of visual processing of image data distortion, the present invention extends the Hank structured technology to high-order tensor visual data, and constructs different image sub-blocks into low-rank tensors with Hank structure; and proposes a low-rank tensor based on low Image Inpainting Method Based on Rank Tensor Ring Decomposition

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
  • Image restoration method based on low-rank tensor ring decomposition and high-order structuralization
  • Image restoration method based on low-rank tensor ring decomposition and high-order structuralization
  • Image restoration method based on low-rank tensor ring decomposition and high-order structuralization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0017] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0018] Based on low-rank tensor ring decomposition and high-order structuring and image restoration method, it includes the following steps:

[0019] Step 1) Input the image to be repaired Determine the area to be repaired in the image and perform a block operation on it. The pixels in the image are divided into known points and unknown points. The known points are the points in the image where the pixels are not 0, and the unknown points are the points where the pixels in the image are 0. point, all unknown points in the image form a set Ω;

[0020] Step 2) constructing a low-rank tensor ring decomposition and a high-order Hank structured model;

[0021] Step 3) Combine the image restoration model built in step 2), repair the color image, and finally reconstruct the output high-quality visual data image

[0022] Described step 2) specif...

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

Disclosed is an image restoration method based on low-rank tensor ring decomposition and high-order structuralization comprises the following steps: 1) inputting a to-be-restored image to determine ato-be-restored area of the image, and 2) constructing a low-rank tensor ring decomposition and high-order Hank structuralization model; and 3) repairing a color image in combination with an image repairing model constructed in the step 2), and finally reconstructing and outputting a high-quality visual data image. The invention has the advantages that image processing efficiency and image restoration accuracy are considered.

Description

technical field [0001] The invention relates to a low-rank tensor ring decomposition and high-order structuring and image restoration method. Background technique [0002] A digital image is a representation of an objective object that contains information about the described object and is an important source of information. However, usually in the process of image data acquisition, the visual quality will be poor due to the influence of various external factors, for example, the damage of hardware equipment, the influence of light and electromagnetic wave interference, etc. In this case, it may also be impossible to directly re-acquire relevant image data due to equipment or time constraints. Therefore, it is a research content with practical application value to restore various blurred, low-resolution, partial pixel loss and other images to obtain high-quality visual data. [0003] Image inpainting is a typical ill-posed problem of image processing, which can be formulat...

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): G06T5/00G06T7/11
CPCG06T7/11G06T2207/10024G06T5/77
Inventor 郑建炜秦梦洁陈婉君徐宏辉黄娟娟陶星朋
Owner ZHEJIANG UNIV OF TECH
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com