Restoration of occluded face image based on improved CycleGAN model
Pages 91 - 97
Abstract
With the rapid development of VR technology in the past two years, VR has been applied to many fields such as communication, video, game and so on. But for VR wearers, their faces are largely obscured, which hinders access to complete facial information. Aiming at the above problems, the neural network generated by cyclic confrontation is used to realize the restoration of the masked face. The neural network can learn the mapping from the face image with VR glasses to that without VR, GAN is used as the generating model, in which the discriminator can judge whether the image is real enough to ensure that the generated image will not lead to deformity, the improved CycleGAN model can make the map learn the distribution transformation of the image, generate the non-occlusion image from the occluded image, learn the mapping of each other, and guarantee not over-fitting. At the same time, the CycleGAN model is implemented by Pytorch Algorithm, and the trained model is applied to the test data set of 500 different faces of celebA.
References
[1]
awaq Generative adversarial nets[C]// International Conference on Neural Information Processing Systems. MIT Press, 2014:2672-2680.
[2]
Zhu J Y, Park T, Isola P, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks[J]. 2017:2242-2251.
[3]
Heusel M, Ramsauer H, Unterthiner T, Gans trained by a two time-scale update rule converge to a local nash equilibrium[C]//Advances in Neural Information Processing Systems.2017: 6626-6637.
[4]
Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.
[5]
Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[J]. 2017.
[6]
Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J]. Computer Science, 2015.
[7]
Guoan Yang, Junjie Yang, Zhengzhi Lu A convolutional neural network with sparse representation[J] Knowledge-Based Systems, 2020, 209.
[8]
JOHNSON J, ALAHI A, LI F F. Perceptual Losses for Real-Time Style Transfer and Super-Resolution[M]// Computer Vision-ECCV 2016. Springer International Publishing, 2016, 694-711.
[9]
Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks[C]//Proceedings of the IEEE
[10]
Conference on Computer Vision and Pattern Recognition. 2019:4401-4410.
[11]
Hertzmann A, Jacobs C E, Oliver N, Image analogies[C]// Conference on Computer Graphics and Interactive Techniques. ACM, 2001:327-340.
[12]
Isola P, Zhu J Y, Zhou T, Image-to-Image Translation with Conditional Adversarial Networks[J]. 2016:5967-5976.
[13]
Pathak D, Krahenbuhl P, Donahue J, Context Encoders: Feature Learning by Inpainting[J].2016:2536-2544.
[14]
Zhou T, Krahenbuhl P, Aubry M, Learning dense correspondence via 3d-guided cycle consistency[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 117-126.
[15]
Kingma D P, Ba J. Adam: A Method for Stochastic Optimization[J]. Computer Science, 2014.
[16]
Kingma D P, Welling M. Auto-Encoding Variational Bayes[J]. 2013.
Recommendations
Lifelong CycleGAN for continual multi-task image restoration
Highlights- A novel lifelong CycleGAN (LCGAN) framework is proposed to continually learn multiple image restoration tasks.
AbstractRecent years have witnessed the great success of deep learning in the applications of image restoration. However, there are still some challenging problems. First, most deep learning methods rely heavily on paired training images which ...
A Robust Image Dehazing Model Using Cycle Generative Adversarial Network with an Improved Atmospheric Scatter Model
Artificial Neural Networks and Machine Learning – ICANN 2024AbstractLearning-based dehazing methods have attained numerous achievements in dehazing images. However, during the dehazing, the image depth and the atmospheric scattering factor are completely ignored. Current models result in the poor quality in ...
Image Restoration Using Gaussian Scale Mixtures in Complex Curvelet Transform Domain
ICMTMA '10: Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 02In this paper, a complex Curvelet transform is presented at first. The key innovation can be generalized as follows:2D and 1D complex wavelet transform instead à trous algorithm sub-band decomposition and ID wavelet transform respectively, and increase ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
April 2021
97 pages
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 June 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
RobCE 2021
RobCE 2021: 2021 International Conference on Robotics and Control Engineering
April 22 - 25, 2021
Tokyo, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 56Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format