Image processing method and system based on sparse auto-encoder, medium and equipment

A sparse auto-encoder and image processing technology, applied in the field of image processing, can solve problems such as loss of important details and features, and achieve the effects of reducing overfitting, realizing image noise reduction, and improving accuracy

Active Publication Date: 2021-01-26
SOUTHWEST UNIVERSITY
View PDF9 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Through the above analysis, the problems and defects of the existing technology are: in the process

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 processing method and system based on sparse auto-encoder, medium and equipment
  • Image processing method and system based on sparse auto-encoder, medium and equipment
  • Image processing method and system based on sparse auto-encoder, medium and equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0067] Aiming at the problems existing in the prior art, the present invention provides an image processing method, system, medium, and device based on a sparse autoencoder. The present invention will be described in detail below in conjunction with the accompanying drawings.

[0068] Such as figure 1 As shown, the image processing method based on the sparse self-encoder provided by the embodiment of the present invention includes:

[0069] S101: The biases of all neural networks are preset to a given vector or matrix, and are constantly updated.

[0070] S102: A cognitive feedback weight matrix is ​​introduced into the ...

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 belongs to the technical field of image processing, and discloses an image processing method, system, medium and device based on a sparse auto-encoder, and the method comprises the stepsof presetting the deviation of all neural networks as a given vector or matrix, and carrying out the continuous updating; introducing a cognitive feedback weight matrix into the deviation, and improving the accuracy of the sparse auto-encoder network by utilizing the feedback deviation; controlling the sparse auto-encoder based on a regularization method of the H1 norm. The invention discloses adenoising sparse auto-encoder based on feedback deviation and H1 regularization. In the model, a feedback bias matrix is introduced, so that the bias of the network can be more accurately controlled.The feedback deviation changes with the number of iterations. H1 regularization can reduce overfitting and can also prevent more important features from being excessively smoothed. Experimental results show that the method is effective and is superior to the existing DnCNN-3, DAEP and EPCNN methods.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image processing method, system, medium, and device based on a sparse autoencoder. Background technique [0002] At present, in image processing, many images are inevitably not affected by noise, and some image processing methods appear accordingly. In image processing, image noise reduction is very important. Image denoising is still challenging at this stage due to image blurring and artifacts that occur during the denoising process. The development of noise reduction algorithms is a necessary task. In most models, the noise is assumed to be Gaussian or salt-and-pepper noise, and many algorithms are designed to remove the effects of reducing noise. [0003] So far, many traditional noise reduction algorithms have appeared, such as: anisotropic dissipation filter, non-local mean filter, BM3D, etc. However, most of these traditional image denoising alg...

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): G06T5/00G06N3/08G06N3/04G06F17/16
CPCG06T5/002G06N3/084G06F17/16G06N3/048G06N3/045Y02T10/40
Inventor 袁建军
Owner SOUTHWEST UNIVERSITY
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