Autoencoders and their application in removing masks

Research Article
Open access

Autoencoders and their application in removing masks

Zixiang Liu 1*
  • 1 Maths department of Imperial College London    
  • *corresponding author lzx556688@hotmail.com
Published on 8 December 2023 | https://doi.org/10.54254/2753-8818/18/20230352
TNS Vol.18
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-201-5
ISBN (Online): 978-1-83558-202-2

Abstract

Images are frequently distorted by noises that have a negative impact on the quality of image data. In this study, the author focuses on coping with a specific type of noise that has arisen regularly in recent years as a result of the pandemic: masks covering portions of the photographs of human faces. The paper employs the autoencoder model, which offers unsupervised learning. It compresses or encodes original data input into a smaller latent vector, then decodes it back to its original size, learning and extracting relevant features from the data in the process. In a further phase, the author employs a combination of convolutional autoencoders and denoising autoencoders, treating masks as corruptions in order to get more accurate predictions regarding the image of a human face without any covering. After training on 2,500 image pairs with and without masks and validating on 200 such image pairs, the model presented in this research achieves an overall accuracy of 93%. The research demonstrates that the combination of convolutional and denoising autoencoders is an excellent method for removing masks from facial images, and the author believes it can also be used to effectively remove other types of noise. However, the study also reveals that the picture data generated in this manner are always inferior to the original, and that the autoencoder can only process data of the same or comparable type on which it has been trained. In the future, improved models will exist to address these shortcomings and be applied to more real-life situations.

Keywords:

convolutional autoencoders, denoising autoencoders, denoising, image reconstruction, mask-removing

Liu,Z. (2023). Autoencoders and their application in removing masks. Theoretical and Natural Science,18,110-117.
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References

[1]. He Chuan, Hu Changhua, Qi Naixin, et al. Fast proximal splitting algorithm for constrained TGV-regularised image restoration and reconstruction[J]. IET Image Processing, 2019, 13(4): 576-582

[2]. Chervinskii. Own work. CC BY-SA 4.0. 2015.

[3]. Kramer, Mark A. "Nonlinear principal component analysis using autoassociative neural networks" (PDF). AIChE Journal, 1991, 37 (2): 233–243.

[4]. Daniel Nelson. What is an Autoencoder. 2020. www.unite.ai.

[5]. Guo, Xifeng & Liu, Xinwang & Zhu, En & Yin, Jianping. Deep Clustering with Convolutional Autoencoders. 2017: 373-382.

[6]. Lilian Weng. From Autoencoder to Beta-VAE. lilianweng.github.io, 2018

[7]. Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. Deep Learning. MIT Press, 2016.

[8]. Vincent, Pascal; Larochelle, Hugo. "Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion". Journal of Machine Learning Research, 2010, 11: 3371–3408.

[9]. Wikipedia contributors, "Autoencoder," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Autoencoder&oldid=1109263923 (accessed September 28, 2022).

Cite this article

Liu,Z. (2023). Autoencoders and their application in removing masks. Theoretical and Natural Science,18,110-117.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics

ISBN：978-1-83558-201-5(Print) / 978-1-83558-202-2(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.18
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. He Chuan, Hu Changhua, Qi Naixin, et al. Fast proximal splitting algorithm for constrained TGV-regularised image restoration and reconstruction[J]. IET Image Processing, 2019, 13(4): 576-582

[2]. Chervinskii. Own work. CC BY-SA 4.0. 2015.

[3]. Kramer, Mark A. "Nonlinear principal component analysis using autoassociative neural networks" (PDF). AIChE Journal, 1991, 37 (2): 233–243.

[4]. Daniel Nelson. What is an Autoencoder. 2020. www.unite.ai.

[5]. Guo, Xifeng & Liu, Xinwang & Zhu, En & Yin, Jianping. Deep Clustering with Convolutional Autoencoders. 2017: 373-382.

[6]. Lilian Weng. From Autoencoder to Beta-VAE. lilianweng.github.io, 2018

[7]. Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. Deep Learning. MIT Press, 2016.

[8]. Vincent, Pascal; Larochelle, Hugo. "Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion". Journal of Machine Learning Research, 2010, 11: 3371–3408.

[9]. Wikipedia contributors, "Autoencoder," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Autoencoder&oldid=1109263923 (accessed September 28, 2022).

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