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

Remote sensing image super resolution method based on dictionary learning

A remote sensing image, super-resolution technology, applied in the field of remote sensing image super-resolution based on dictionary learning, can solve the problem of slow reconstruction speed and so on

Active Publication Date: 2016-11-23
NANJING UNIV OF SCI & TECH
View PDF10 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the larger observation range of remote sensing images, richer texture information, and richer landform features of different types of objects, in order to achieve a certain reconstruction effect, the size of the dictionary will be increased to ensure the number of effective atoms, resulting in excessive reconstruction speed. slow

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
  • Remote sensing image super resolution method based on dictionary learning
  • Remote sensing image super resolution method based on dictionary learning
  • Remote sensing image super resolution method based on dictionary learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] In order to better understand the technical content of the present invention, specific embodiments are given together with the attached drawings for description as follows.

[0058] Aspects of the invention are described in this disclosure with reference to the accompanying drawings, which show a number of illustrated embodiments. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of numerous ways, since the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects of the present disclosure may be used alone or in any suitable combination with other aspects of the present disclosure.

[0059] This disclosure uses the idea of ​​object classification to propose a super-resolution method based on a classificati...

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 remote sensing image super resolution method based on dictionary learning, comprising the following steps: S1, performing dictionary learning of corresponding feature types based on an image library of different feature types; S2, identifying the feature type of an original image; and S3, carrying out an image super resolution reconstruction process using a corresponding feature type dictionary. The invention puts forward a super resolution method based on dictionary learning using the idea of feature classification. As the number of atoms in a dictionary used in the reconstruction process is reduced greatly, the dictionary scale is reduced greatly. The proportion of effective atoms in the classification dictionary used is increased significantly, so that the number of effective atoms in the dictionary used in the reconstruction process is almost the same as a general dictionary, and the speed of reconstruction is improved significantly while the quality of reconstruction is ensured. The process only involves the adjustment of parameters, the computation efficiency is high, and the reconstruction quality of remote sensing images is high.

Description

technical field [0001] The invention relates to the technical field of remote sensing imaging, in particular to a method for super-resolution of remote sensing images based on dictionary learning. Background technique [0002] With the development of computer science and space science, remote sensing technology, especially remote sensing imaging, has developed rapidly. Image spatial resolution is a key index for evaluating the quality of remote sensing images, and also an important parameter in the application of remote sensing imaging, which is crucial in the acquisition and application of images. Compared with traditional low-resolution remote sensing images, high-resolution remote sensing images can clearly express the characteristic distribution and spatial correlation of ground objects, and can distinguish the more detailed structural composition of ground objects, providing a basis for interpretation and analysis. Excellent condition and foundation. In recent years, ...

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 Applications(China)
IPC IPC(8): G06T3/40
Inventor 孙权森王超刘亚洲张从梅
Owner NANJING UNIV OF SCI & 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

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