Use of AI to Recreate and Repatriate Lost, Destroyed or Stolen Paintings: The 1785 Parisian Salon Case Study

O'Brien, C. and Hutson, J. and Olsen, T. and Ratican, J. (2023) Use of AI to Recreate and Repatriate Lost, Destroyed or Stolen Paintings: The 1785 Parisian Salon Case Study. Journal of Innovation and Technology, 2023 (21). pp. 1-12. ISSN 2805-5179

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Abstract

This study investigates the efficacy of artificial intelligence (AI) in the field of artwork restoration, focusing on lost, stolen, or destroyed artworks. Employing a dual approach that combines traditional manual restoration techniques with advanced generative AI tools, the research centers on a case study of the 1785 Parisian Salon. Itspecifically examines the reconstitution of Antoine François Callet's painting, Achilles Dragging the Body of Hector, unveiled alongside Jacques-Louis David's Oath of the Horatii. The study utilizes Easy Diffusion and Stable Diffusion 2.1 technologies for inpainting and colorization processes. These AI tools are employed in concert with manual restoration practices to recreate the Callet painting. The methodology also includes the use of secondary visual materials, such as Pietro Martini's 1785 engraving of the Salon Carré, to inform the AI's trained dataset. The application of generative AI in this context significantly accelerates the restoration process. However, the study identifies a critical issue where successive AI-based inpainting iterations lead to a degradation in color fidelity and detail precision. This degradation is evidenced by the emergence of unintended artifacts and a loss of visual coherence in the restored images. While AI significantly expedites the artwork restoration process, its integration with manual techniques is crucial to mitigate the loss of artistic detail and color accuracy. The study's findings emphasize the need for a balanced approach that leverages the strengths of both AI and traditional restoration methods. This integrative strategy is essential for preserving the original artistic essence of artworks, contributing significantly to the fields of art restoration and digital humanities.

Item Type:	Article
Uncontrolled Keywords:	artwork restoration, digital restoration, conservation, generative AI, stable diffusion
Subjects:	N Fine Arts > NC Drawing Design Illustration Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	12 Dec 2023 06:07
Last Modified:	31 Jan 2024 02:07
URI:	http://eprints.intimal.edu.my/id/eprint/1883

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