Abstract
Research on Image inpainting has attracted growing attention as image data and applications become commonplace. Nevertheless, it is still a challenge to repair or restore missing parts of electronic images to its original state. In this paper, we develop a new deep learning based model by combining the two mainstream generative models: Auto-encoders and Generative Adversarial Networks to improve the effect of image inpainting. Specifically we design a novel joint loss function which take into consideration reconstruction loss in a Generator and adversarial loss in a Discriminator to improve the repaired effect of texture features and semantic information. The model and joint loss function were experimented and evaluated using the public face data set celebA. Initial results showed that our model and function has significantly improved the effect of repairment in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics.
Original language | English |
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Title of host publication | 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-6297-6 |
ISBN (Print) | 978-1-6654-6298-3 |
DOIs | |
Publication status | Published (in print/issue) - 13 Sep 2022 |
Event | International Conference on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress - Falerna, Italy Duration: 12 Sep 2022 → 15 Sep 2022 |
Conference
Conference | International Conference on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress |
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Country/Territory | Italy |
City | Falerna |
Period | 12/09/22 → 15/09/22 |
Keywords
- Image inpaining
- Auto-encode
- Generative Adversarial Network
- Deep learning
- Training
- Measurement
- Visualization
- PSNR
- Semantics
- Neural networks