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

A Multi-frame Video Super-resolution Method Fused with Attention Mechanism

A multi-frame super-resolution and super-resolution technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as complex motion, achieve enhanced expression ability, improve accuracy, and improve super-resolution effects Effect

Active Publication Date: 2020-12-22
SUN YAT SEN UNIV
View PDF6 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The scene in the video has complex motion, and even often faces scene switching, which requires the super-resolution network to deal with this situation adaptively. The existing three methods have certain defects in the alignment between frames

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
  • A Multi-frame Video Super-resolution Method Fused with Attention Mechanism
  • A Multi-frame Video Super-resolution Method Fused with Attention Mechanism
  • A Multi-frame Video Super-resolution Method Fused with Attention Mechanism

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0028] In the existing multi-frame video super-resolution task, it is necessary to use the information of adjacent frames to complement the current frame, and learn the mapping relationship from low-resolution images to high-resolution images from the features of multiple frames. The difficulty in obtaining good super-resolution results is the alignment of adjacent frame features and the effective use of aligned features. Previous methods extract multi-frame features through feature cascading, three-dimensional convolution or cyclic neural network, but these methods have their own shortcomings, and it is difficult to fully extract the effective features of each adjacent frame, resulting in the appearance of frame after super-resolution video. The phenomenon of dis...

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 multi-frame video super-resolution method with fusion attention mechanism, comprising: collecting video data and using video enhancement technology to train the video data to generate a training set and a test set; connecting a deformation convolution feature alignment module and The feature reconstruction module is used to form a multi-frame super-resolution network, and the training set is used to train the multi-frame super-resolution network; the 3D convolution feature alignment module is added to the multi-frame super-resolution network, and the training set is used to train the multi-frame super-resolution network. The network is trained; the feature fusion module is added to the multi-frame super-resolution network, and the training set is used to train the multi-frame super-resolution network; the training set is used to fine-tune the multi-frame super-resolution network to generate a multi-frame super-resolution model ; The multi-frame super-resolution model is tested on the test set. The present invention can effectively improve the effect of super-resolution through the analysis of big data.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a multi-frame video super-resolution method. Background technique [0002] Super-resolution technology is widely used in public security monitoring and identification, medical imaging, satellite remote sensing, virtual reality and other practical scenarios. Due to the development of visual media display technology, image and video data urgently need to have a better display or playback effect on existing high-definition displays, which also puts forward higher requirements for super-resolution technology. Compared with single-frame super-resolution, the video super-resolution task adds timing information. According to different ways of utilizing timing information, deep learning-based video super-resolution techniques can be roughly divided into methods based on multi-frame cascading, methods based on 3D convolution, and methods based on loop structures. [0003] The ...

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): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06T3/4046G06N3/08G06N3/045
Inventor 周凡苏卓林谋广陈小燕
Owner SUN YAT SEN UNIV
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