Photorealistic Texture Contextual Fill-In
This paper presents a comprehensive study of the application of AI-driven inpainting techniques to the restoration of historical photographs of the Czech city Most, with a focus on restoration and reconstructing the lost architectural heritage. The project combines state-of-the-art methods, includin...
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MDPI AG
2024-12-01
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| Series: | Heritage |
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| Online Access: | https://www.mdpi.com/2571-9408/8/1/9 |
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| author | Radek Richtr |
| author_facet | Radek Richtr |
| author_sort | Radek Richtr |
| collection | DOAJ |
| description | This paper presents a comprehensive study of the application of AI-driven inpainting techniques to the restoration of historical photographs of the Czech city Most, with a focus on restoration and reconstructing the lost architectural heritage. The project combines state-of-the-art methods, including generative adversarial networks (GANs), patch-based inpainting, and manual retouching, to restore and enhance severely degraded images. The reconstructed/restored photographs of the city Most offer an invaluable visual representation of a city that was largely destroyed for industrial purposes in the 20th century. Through a series of blind and informed user tests, we assess the subjective quality of the restored images and examine how knowledge of edited areas influences user perception. Additionally, this study addresses the technical challenges of inpainting, including computational demands, interpretability, and bias in AI models. Ethical considerations, particularly regarding historical authenticity and speculative reconstruction, are also discussed. The findings demonstrate that AI techniques can significantly contribute to the preservation of cultural heritage, but must be applied with careful oversight to maintain transparency and cultural integrity. Future work will focus on improving the interpretability and efficiency of these methods, while ensuring that reconstructions remain historically and culturally sensitive. |
| format | Article |
| id | doaj-art-2bf167401b044720bfd6ba9c83b509e5 |
| institution | Kabale University |
| issn | 2571-9408 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Heritage |
| spelling | doaj-art-2bf167401b044720bfd6ba9c83b509e52025-01-24T13:34:18ZengMDPI AGHeritage2571-94082024-12-0181910.3390/heritage8010009Photorealistic Texture Contextual Fill-InRadek Richtr0Faculty of Information Technology, Czech Technical University in Prague, 160 00 Praha, Czech RepublicThis paper presents a comprehensive study of the application of AI-driven inpainting techniques to the restoration of historical photographs of the Czech city Most, with a focus on restoration and reconstructing the lost architectural heritage. The project combines state-of-the-art methods, including generative adversarial networks (GANs), patch-based inpainting, and manual retouching, to restore and enhance severely degraded images. The reconstructed/restored photographs of the city Most offer an invaluable visual representation of a city that was largely destroyed for industrial purposes in the 20th century. Through a series of blind and informed user tests, we assess the subjective quality of the restored images and examine how knowledge of edited areas influences user perception. Additionally, this study addresses the technical challenges of inpainting, including computational demands, interpretability, and bias in AI models. Ethical considerations, particularly regarding historical authenticity and speculative reconstruction, are also discussed. The findings demonstrate that AI techniques can significantly contribute to the preservation of cultural heritage, but must be applied with careful oversight to maintain transparency and cultural integrity. Future work will focus on improving the interpretability and efficiency of these methods, while ensuring that reconstructions remain historically and culturally sensitive.https://www.mdpi.com/2571-9408/8/1/9cultural heritageimage reconstructioncolorizationurbanMostinpainting |
| spellingShingle | Radek Richtr Photorealistic Texture Contextual Fill-In Heritage cultural heritage image reconstruction colorization urban Most inpainting |
| title | Photorealistic Texture Contextual Fill-In |
| title_full | Photorealistic Texture Contextual Fill-In |
| title_fullStr | Photorealistic Texture Contextual Fill-In |
| title_full_unstemmed | Photorealistic Texture Contextual Fill-In |
| title_short | Photorealistic Texture Contextual Fill-In |
| title_sort | photorealistic texture contextual fill in |
| topic | cultural heritage image reconstruction colorization urban Most inpainting |
| url | https://www.mdpi.com/2571-9408/8/1/9 |
| work_keys_str_mv | AT radekrichtr photorealistictexturecontextualfillin |