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

Image adaptive denoising method based on attention mechanism

An attention and self-adaptive technology, applied in the field of image processing, can solve problems such as limited effects

Active Publication Date: 2020-06-09
WUHAN UNIV
View PDF5 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method only focuses on the coherent speckle noise of SAR images, and has limited effect on other noises

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 adaptive denoising method based on attention mechanism
  • Image adaptive denoising method based on attention mechanism
  • Image adaptive denoising method based on attention mechanism

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] The technical scheme of the present invention can adopt software technology to realize automatic flow operation. The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0047] figure 1 It is a flow chart of the present invention. An attention mechanism-based image adaptive denoising method provided by an embodiment of the present invention is divided into a training phase and a testing phase. The specific implementations are as follows:

[0048]The training phase includes: constructing a training data set, constructing an adaptive denoising convolutional neural network based on the attention mechanism, which is composed of a denoising network and an attention mechanism network, and using the training data set to optimize the network model to train.

[0049] For the specific training process of the embodiment, see figure 2 , including the following steps:

[0050] Step...

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 adaptive denoising method based on an attention mechanism, and the method comprises the construction of image denoising and a convolutional neural network based on theattention mechanism, and the network is mainly composed of two parts which respectively complete the noise image extraction and noise image weight analysis. A natural image is used as the input of the network, wherein the noise image extraction part completes extraction of a noise image, noise image weight analysis is of an action structure, the weight of noise distribution is learned to obtain aweight map of noise, and finally, the noise image extraction part and the noise image analysis part are combined to obtain a denoised image by combining the noise image and the input noise image. Thedepth image convolutional neural network is based on an attention mechanism, and the learning efficiency of the network is adaptively improved. According to the method, denoising work can be carriedout on the image containing noise, and a good visual effect is achieved.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image denoising method, in particular to an attention mechanism-based image self-adaptive denoising method. Background technique [0002] Today's society is a rapidly developing information society, and information carriers are becoming more and more abundant, but images are still the most common and important information carriers. According to relevant statistics, in human daily life, more than 80% of the information obtained from the outside world comes from vision. Images generally contain a large amount of information, and the dissemination of images is very convenient and fast, which makes images a very important source of information, which exists in various places in daily life and has become an important data carrier in today's society. [0003] Image denoising is the use of various technical means to filter noise to improve the quality of the image so that the image can...

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
CPCG06T2207/20081G06T2207/20084G06T5/70
Inventor 陈军黄志兵
Owner WUHAN 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