Novel Approach to Protect Red Revolutionary Heritage Based on Artificial Intelligence Algorithm and Image-Processing Technology
Auteur(s): |
Junbo Yi
Yan Tian Yuanfei Zhao |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 25 août 2024, n. 9, v. 14 |
Page(s): | 3011 |
DOI: | 10.3390/buildings14093011 |
Abstrait: |
The red revolutionary heritage is a valuable part of China’s historical and cultural legacy, with the potential to generate economic benefits through its thoughtful development. However, challenges such as insufficient understanding, lack of comprehensive planning and layout, and limited protection and utilization methods hinder the full realization of the political, cultural, and economic value of red heritage. To address these problems, this paper thoroughly examines the current state of red revolutionary heritage protection and identifies the problems within the preservation process. Moreover, it proposes leveraging advanced artificial intelligence (AI) technology to repair some damaged image data. Specifically, this paper introduces a red revolutionary cultural relic image-restoration model based on a generative adversarial network (GAN). This model was trained using samples of damaged image and utilizes high-quality models to restore these images effectively. The study also integrates real-world revolutionary heritage images for practical application and assesses its effectiveness through questionnaire surveys. The survey results show that AI algorithms and image-processing technologies hold significant potential in the protection of revolutionary heritage. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10804704 - Publié(e) le:
10.11.2024 - Modifié(e) le:
10.11.2024