GAN-based algorithm for efficient image inpainting

Zhengyang Han; Zehao Jiang; Yuan Ju

doi:10.1117/12.2671788

28 April 2023 GAN-based algorithm for efficient image inpainting

Zhengyang Han, Zehao Jiang, Yuan Ju

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126101T (2023) https://doi.org/10.1117/12.2671788
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the Generative Adversarial Network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model.

Citation Download Citation

Zhengyang Han, Zehao Jiang, and Yuan Ju "GAN-based algorithm for efficient image inpainting", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126101T (28 April 2023); https://doi.org/10.1117/12.2671788

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KEYWORDS

Education and training

Gallium nitride

Data modeling

Machine learning

Facial recognition systems

Image restoration

Batch normalization

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