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

Method for repairing face defect images based on auto-encoder and generative adversarial networks

A face image and self-encoder technology, applied in the field of image processing, can solve problems such as blurred image details, unsmooth images, complex image restoration and repair model design, and achieve the effect of improving clarity

Active Publication Date: 2018-09-11
XIANGTAN UNIV
View PDF7 Cites 85 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] In view of the complex design of image restoration and repair models in the prior art, the speed is slow, the efficiency is low, and it is easy to cause blurred image details, unsmooth images, and poor effects. The present invention provides a self-encoder-based A method for repairing face defect images with generative adversarial networks

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
  • Method for repairing face defect images based on auto-encoder and generative adversarial networks
  • Method for repairing face defect images based on auto-encoder and generative adversarial networks
  • Method for repairing face defect images based on auto-encoder and generative adversarial networks

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0058] Taking the CelebA face image dataset (178 pixels*218 pixels) as an example, when doing research on the restoration of the defect area of ​​the images in the CelebA face image dataset, we first need to select the training data set and the test data set, and combine them Perform preprocessing; use the processed data set to train the autoencoder model and the conditional generation network model respectively; then input the defective image into the trained autoencoder to obtain the filling content based on the information around the defect area; the filling content generated by the autoencoder The content is filled into the defective area of ​​the defective face image, and the obtained complete image input condition generates an adversarial network, so as to obtain a clear and natural restoration result. This example is the face defect image restoration process in the CelebA face image dataset.

[0059] The experimental environment is based on a GPU high-performance server...

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 present invention provides a method for repairing face defect images based on an auto-encoder and generative adversarial networks. Through combination of the auto-encoder and the generative adversarial networks, the method comprises the following steps of: (1) performing face data set defect preprocessing; (2) employing the data set after processing to train the auto-encoder to allow the auto-encoder to reach the optimal state; (3) employing the data set after processing to train the condition generative adversarial networks to allow the condition generative adversarial networks to reach the optimal state; (4) inputting a defect image to be repaired into the trained encoder to generate a face image to be repaired; and (5) inputting the image to be repaired to the condition generative adversarial networks to generate much clearer and more natural restored face image. The method improves the restoring definition of the defect face area and the fidelity of the defect content, avoids the pseudomorphism of the defect area edges to the maximum extent, restricts the generation direction of the defect area and generates much clearer and more natural restoring effect.

Description

technical field [0001] The invention relates to a method for repairing a human face defect image, in particular to a method for repairing a human face defect image based on an autoencoder and a generated confrontation network, and belongs to the technical field of image processing. Background technique [0002] Face recognition technology has developed significantly in recent years. However, recognizing partially occluded faces is still a challenge for existing face recognition technologies. In real applications, there is an increasing demand for occluded image inpainting, such as surveillance and security. Image restoration, as a common image editing operation, aims to fill missing or masked areas in an image with plausible content. The resulting content can be as accurate as the original or completely conform to the overall image, making the restored image look real. Due to the inherent ambiguity and complexity of natural images, image restoration (image filling) has be...

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): G06T5/00G06K9/00G06N3/04
CPCG06V40/161G06V40/168G06V40/172G06N3/045G06T5/00
Inventor 唐欢容刘恋欧阳建权
Owner XIANGTAN 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