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

Super-resolution image reconstruction method using analysis sparse representation

A low-resolution image and sparse representation technology, applied in the field of super-resolution image reconstruction using analytical sparse representation, can solve the problems that analytical sparse representation has not been proposed by others

Active Publication Date: 2015-04-01
CHINACCS INFORMATION IND
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

So far, analytically sparse representations have not been proposed by others in the field of super-resolution reconstruction

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
  • Super-resolution image reconstruction method using analysis sparse representation
  • Super-resolution image reconstruction method using analysis sparse representation
  • Super-resolution image reconstruction method using analysis sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0064] The super-resolution image reconstruction method based on analytical sparse representation proposed by the present invention is described in detail in conjunction with the accompanying drawings and embodiments as follows:

[0065] The embodiment of the super-resolution image reconstruction method based on analytical sparse representation of the present invention consists of two parts: dictionary training and super-resolution image reconstruction, and its flow is as follows figure 1 As shown, among them,

[0066] The first part of dictionary training includes the following steps:

[0067] 11) Set the training parameters, including the image magnification A required by the user (A>1, the specific value is specified according to actual needs, the value in this embodiment is 2), the high-resolution image block h S of size a1 and low-resolution image patch l S The size a2 is a1=A×a2 (the size of y is generally set within 20×20 pixels to ensure the effect and operation spee...

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 relates to a super-resolution image reconstruction method based on analysis sparse representation, belonging to the technical field of image processing. The method comprises the following steps of: performing dictionary training according to a training sample set; and training a high-resolution dictionary and a low-resolution dictionary for an extracted feature; converting an image to be input from an RGB (Red, Green and Blue) space into a 1 alpha beta space and dividing into blocks of a same size; performing two kinds of operation on the blocks, wherein one is that each block is amplified by using the conventional amplification method and the other one is that an residual image of each block is extracted, sparse representation of the residual image in the low-resolution dictionary is calculated, and then the residual image is reconstructed in the high-resolution dictionary to obtain a reconstructed residual image; summarizing results of the two steps, converting back into the RGB space and performing back projection to obtain the reconstructed super-resolution image. According to the method, the image reconstruction noise can be obviously reduced, and detail features are kept; and meanwhile, the method has the advantages of easiness in operation and wide application.

Description

technical field [0001] The invention belongs to the technical field of image resolution enhancement, in particular to a super-resolution image reconstruction method using analytical sparse representation. Background technique [0002] In a large number of electronic imaging applications, high resolution images are often desired. High resolution means a high density of pixels in an image, providing more detail that is essential in many practical applications. For example, high-resolution medical images are very helpful for doctors to make correct diagnoses; using high-resolution satellite images, it is easy to distinguish similar objects from similar objects; if high-resolution images can be provided, patterns in computer vision The recognition performance will be greatly improved. Since the 1970s, charge-coupled devices (CCDs), CMOS image sensors have been widely used to capture digital images. Although these sensors are suitable for most imaging applications, current res...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40G06T5/50G06K9/66
Inventor 宁强舒泓新陈侃弋力范楚楚陆垚温江涛
Owner CHINACCS INFORMATION IND
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

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com