Research on Conservation and Restoration Methods of Museum Artifacts in the Context of Artificial Intelligence

Abstract

Abstract In the era of artificial intelligence, the conservation and restoration of museum artifacts encounter both new possibilities and hurdles. The limitations of conventional conservation techniques, in terms of efficiency and accuracy, are becoming increasingly apparent. This study explores the potential of 3D reconstruction technologies, SIFT algorithms, and Poisson’s equation to revolutionize the digital restoration of cultural relics. These advanced approaches allow for precise, efficient restorations and the ability to undertake complex projects without risking harm to the original items. Our approach integrates cutting-edge algorithms to tailor 3D reconstruction strategies for museum artifacts, culminating in creating a comprehensive digital artifact database. Through the innovative use of feature point detection and texture synthesis, we have achieved notable successes, including a 12% improvement in 3D model accuracy and a significant enhancement in the automation of the restoration process. With an SSIM value of 0.9964 for restored images, our method demonstrates superiority over traditional restoration techniques, marking a significant stride towards the efficient digital preservation of cultural heritage.