Image super-resolution method based on dictionary learning and non-local total variation

A dictionary learning, non-local technology, applied in the field of image processing, can solve the problems of contradictory edge texture, ringing effect on the edge of high-resolution images, loss of high-frequency information, etc., to shorten the training time, shorten the image reconstruction time, The effect of improving the efficiency of reconstruction

Inactive Publication Date: 2013-06-05
XIDIAN UNIV
View PDF4 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In the document Image super-resolution as sparse representation of raw image patches, Yang et al. of the University of Illinois in the United States proposed to use dictionary learning and sparse representation theory to realize the image of a single frame image. Super-resolution, in this method, due to the sparse representation ov

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 super-resolution method based on dictionary learning and non-local total variation
  • Image super-resolution method based on dictionary learning and non-local total variation
  • Image super-resolution method based on dictionary learning and non-local total variation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] The concrete realization and effect of the present invention are described in further detail below with reference to the accompanying drawings:

[0039] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0040] Step 1. Train the dictionary

[0041] (1a) Extract 100,000 pairs of image blocks with a size of 8x8 from an image training set containing 91 images, and construct the 100,000 pairs of image blocks into a high-resolution image block matrix X with a size of 81x99966 h and a low-resolution image block matrix X of size 144x99966 l ;

[0042] (1b), use the KSVD algorithm to solve and train a high-resolution dictionary D h and a low-resolution dictionary D l , the original KSVD algorithm formula is min [ D , Z ] { | | X - ...

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 an image super-resolution method based on dictionary learning and non-local total variation. The image super-resolution method based on the dictionary learning and the non-local total variation mainly solves the problems of ring effect, lost high-frequency information, and inaccuracy of boundary matching of a super-resolution method and the like. The achieving steps include (1), inputting an image training set; (2) using a KSVD algorithm to train two corresponding high-resolution dictionary and a low-resolution dictionary; (3) conducting sparse representation of a low-resolution input image, and determining sparse coefficient; (4) using determined sparse coefficient and the high-resolution input image to obtain a high-resolution image; (5) conducting ring effect removal of the non-local total variation on the reconstructed high-resolution image; (6) conducting high-frequency information enhancing of the high-resolution image by error compensation, and obtaining a final result. By showing of a simulation experiment, compared with the prior art, the image super-resolution method based on the dictionary learning and the non-local total variation has the advantages of being simple in operation, small in noise, clear in edge and the like, and can be used for obtaining a high-definition image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution method, especially an image super-resolution method based on dictionary learning and non-local total variation, which can be used to improve the resolution of natural images. Background technique [0002] Image resolution is an important indicator of image quality. With the invention of charge-coupled element and complementary metal-oxide-semiconductor image sensor, people have made certain progress in the quality of acquired images, but image sensors are vulnerable to blur, undersampling, noise, etc. factors, so it is difficult to further improve the image quality. People hope to obtain high-resolution images by improving the image sensor from the hardware aspect, but the cost of this method is too expensive and difficult to popularize. Therefore, some people propose to use image super-resolution method to improve the acquired image resolution....

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
Inventor 郑喆坤焦李成鞠军委孙增增谷爱国马文萍马晶晶
Owner XIDIAN 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