ITM Web Conf.
Volume 54, 20232nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|Number of page(s)||12|
|Published online||04 July 2023|
Artwork restoration using paired image translation-based generative adversarial networks
Dept. of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
* Corresponding author, email id- email@example.com, CCET (Degree Wing), Sector 26, Chandigarh-160019
Preservation of the artworks has historical and cultural importance. However, with time, environmental factors severely affect artworks, and these damages are often complicated to repair manually and through traditional methods. We propose a method to restore artwork that has been damaged over time. This work proposes a systematic approach using paired image-to-image translation based on a generative adversarial network. The experimental results have been quantitatively evaluated. The experimental results obtained from the presented work visually prove that the presented approach of artwork restoration completely restores the damaged artwork.
Key words: Image translation / generative adversarial networks / image restoration / deep learning / image inpainting / artwork restoration
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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