Sparse representation image reconstruction method based on Gaussian scale structure block grouping

A Gaussian scale, sparse representation technology, applied in image generation, image data processing, graphics and image conversion, etc., can solve problems such as unpredictable external environment, achieve edge detail preservation, high image peak signal-to-noise ratio, high similarity quality effect

Inactive Publication Date: 2017-08-11
HUBEI UNIV OF TECH
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the process of imaging, transmission, conversion, storage, reproduction and display of images, due to the inherent physical limitations of ima

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
  • Sparse representation image reconstruction method based on Gaussian scale structure block grouping
  • Sparse representation image reconstruction method based on Gaussian scale structure block grouping
  • Sparse representation image reconstruction method based on Gaussian scale structure block grouping

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The above-mentioned content of the present invention will be described in further detail below through the embodiment form, but this should not be interpreted as the scope of the above-mentioned theme of the present invention is limited to the following embodiments, all technologies realized based on the above-mentioned content of the present invention belong to this invention the scope of the invention.

[0029] Such as figure 1 As shown, the sparse representation image reconstruction method based on Gaussian scale structure block grouping of the present invention comprises the following steps:

[0030] Step 1, using the non-local self-similar model trained from the natural image, mixing the non-local similar block into the group obtained by the prior model method, and using the search method to extract the optimal block grouping model;

[0031] Step 2, combining the block grouping model and the non-locally extended Gaussian scale mixture model, using the alternating ...

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 provides a sparse representation image reconstruction method based on Gaussian scale structure block grouping. The method comprises the following steps of using the non-local self-similar model trained from the natural image, mixing the non-local similar blocks into the group obtained by the priori model method, and extracting the optimal block grouping model by the search method; combining the block grouping model and the non-local extension Gaussian scale mixing model, using the alternating minimization method for synchronous sparse coding, and solving the update image block; associating the block grouping model and the Gaussian scale mixing model to a coding framework, using the selected training dictionary to calculate the image reconstruction update solution obtained by the association model, sending the update solution value to the block grouping model for carrying out the step one and the step two again, repeating the steps until the optimal solution is obtained, and outputting the optimal solution of image reconstruction. The reconstructed image obtained by the method has good maintenance performance of details such as the edge and the texture, and has excellent the peak signal to noise ratio quality.

Description

technical field [0001] The invention belongs to the technical field of image super-resolution reconstruction, and in particular relates to a sparse representation image reconstruction method based on Gaussian scale structure block grouping. Background technique [0002] With the rapid development of the information age, digital images are widely used due to their good performance, and have become one of the most important carriers for human beings to transmit information. However, in the process of imaging, transmission, conversion, storage, reproduction and display of images, due to the inherent physical limitations of imaging equipment itself and the influence of unfavorable factors such as unpredictable external environments, the acquired images are often degraded images. . [0003] In order to restore useful information in degraded images, image super-resolution restoration technology has become a research hotspot in the fields of computer vision, computer graphics, etc...

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): G06T11/00G06T3/40G06K9/46
CPCG06T3/4053G06T11/001G06T2211/416G06V10/40G06V10/513
Inventor 武明虎鲁亚琪刘敏赵楠刘聪孔祥斌陈瑞李然陈泽昊宋冉冉饶哲恒
Owner HUBEI UNIV OF TECH
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