Image Inpainting Technique Based on Smart Terminal: A Case Study in CPS Ancient Image Data

• Published in: IEEE access Vol. 7; pp. 69837 - 69847
• Main Authors: Weng, Yu, Zhou, Haiwen, Wan, Jian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: character image; Cloud computing; Computational modeling; conditional generative adversarial network; CPS data; Cyber-physical systems; Data models; Digital imaging; Electronic devices; Gallium nitride; Generative adversarial networks; Handwriting; Image quality; Image restoration; Libraries; Maintenance engineering; Mathematical models; Museums; smart terminal
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2919326

Cyber-physical system (CPS) can intelligently feel the interactive information between data and terminal. The combination of CPS data and intelligent terminal can make terminal devices communicate with each other, coordinate operation, and share information in a complex environment. At present, the demand for smart terminal technology in university libraries and national cultural museums is also increasing. However, the data for smart terminal display and development of shared services face the problem that the original data is difficult to collect and the incomplete images are difficult to repair. In order to display the ancient books in a smart terminal, we have established a model for image inpainting through deep learning. In this paper, we set up a smart terminal display solution to display the digital protection effect of incomplete character images. Then, we propose a model based on Conditional Generative Adversarial Network (CGAN) to solve the problem of repairing incomplete character images. We put this model into the computing module and deployed it to the cloud computing platform of the smart terminal. Moreover, we construct a handwritten Yi character image data set in the cloud. To verify the CGAN calculation model, we design and construct two quality detection tests. According to the experimental results, the image inpainting quality of the CGAN is better than the traditional image restoration technology. Based on the experimental analysis and evaluation criteria, we find that the CGAN calculation model is of great significance for ancient book inpainting.