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

Two-dimension analysis thinning model and dictionary training method and image denoising method thereof

A sparse model, two-dimensional analysis technology, applied in the field of signal modeling, can solve a large number of problems, two-dimensional spatial structure damage, local correlation between images is not effectively used, etc.

Active Publication Date: 2013-09-04
BEIJING UNIV OF TECH
View PDF3 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the past, when all the analysis sparse models dealt with two-dimensional images, they converted them into one-dimensional high-dimensional signals by columns or rows, which caused the following problems: first, the two-dimensional spatial structure of the image was destroyed, and the local Correlation is not exploited effectively, and secondly, in order to get an effective robust estimate, a large number of training samples in high-dimensional space are required

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
  • Two-dimension analysis thinning model and dictionary training method and image denoising method thereof
  • Two-dimension analysis thinning model and dictionary training method and image denoising method thereof
  • Two-dimension analysis thinning model and dictionary training method and image denoising method thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] This two-dimensional analytical sparse model, the model is the formula (2)

[0035] { Ω ^ 1 , Ω ^ 2 , { X ^ j } j = 1 M } = arg min Ω 1 , Ω 2 , { X j } j = 1 M ...

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 two-dimension analysis thinning model and a dictionary training method and an image denoising method based on the two-dimension analysis thinning model, wherein the two-dimension analysis thinning model fully utilizes the spatial correlation of images, needs fewer training samples, and greatly saves the storage space of a dictionary.

Description

technical field [0001] The invention belongs to the technical field of signal modeling, and in particular relates to a two-dimensional analysis sparse model, a dictionary training method and an image denoising method based on the model. Background technique [0002] Sparse representation is a relatively mature modeling method that has been widely studied and widely used in most signal processing fields, such as image denoising, texture synthesis, video processing and image classification. Using sparse representation to signal Modeling mainly includes two categories: synthetic sparse modeling and analytical sparse modeling. [0003] The synthetic model is defined as follows: x=Db,s.t.||b|| 0 = k, here is an overcomplete dictionary in which each column represents an atom (primitive). is a sparse vector. l 0 Norm||·|| 0 The sparsity used to characterize the sparse signal is defined as k non-zero elements in a vector. In the synthetic model, the signal x can be repres...

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): G06T7/00
Inventor 施云惠齐娜丁文鹏尹宝才
Owner BEIJING UNIV OF TECH
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