Research into Digital Oil Painting Restoration Algorithm Based on Image Acquisition Technology

Jiang, Dengpan; Xie, Hui

doi:10.1109/icdiime56946.2022.00022

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…In the broader field of material culture, AI systems have been used for the classification and/or reconstruction of pottery [46][47][48][49][50][51][52][53][54][55], and provenance studies of pottery [55], as well as the classification of ancient coins [56], constituent materials in shell middens [57], or of cut marks on bones retrieved from archaeological sites sites [58,59]. Custom -designed AI systems have also been used in the field of restoration and conservation [55,60,61].…”

Section: Use Of Genai Language Models In Cultural Heritage Studiesmentioning

confidence: 99%

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Spennemann

2024

Heritage

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Generative artificial intelligence (genAI) language models have become firmly embedded in public consciousness. Their abilities to extract and summarise information from a wide range of sources in their training data have attracted the attention of many scholars. This paper examines how four genAI large language models (ChatGPT, GPT4, DeepAI, and Google Bard) responded to prompts, asking (i) whether artificial intelligence would affect how cultural heritage will be managed in the future (with examples requested) and (ii) what dangers might emerge when relying heavily on genAI to guide cultural heritage professionals in their actions. The genAI systems provided a range of examples, commonly drawing on and extending the status quo. Without a doubt, AI tools will revolutionise the execution of repetitive and mundane tasks, such as the classification of some classes of artifacts, or allow for the predictive modelling of the decay of objects. Important examples were used to assess the purported power of genAI tools to extract, aggregate, and synthesize large volumes of data from multiple sources, as well as their ability to recognise patterns and connections that people may miss. An inherent risk in the ‘results’ presented by genAI systems is that the presented connections are ‘artifacts’ of the system rather than being genuine. Since present genAI tools are unable to purposively generate creative or innovative thoughts, it is left to the reader to determine whether any text that is provided by genAI that is out of the ordinary is meaningful or nonsensical. Additional risks identified by the genAI systems were that some cultural heritage professionals might use AI systems without the required level of AI literacy and that overreliance on genAI systems might lead to a deskilling of general heritage practitioners.

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Section: Use Of Genai Language Models In Cultural Heritage Studiesmentioning

confidence: 99%

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Spennemann

2024

Heritage

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“…In the 1940s and 1950s, someone came up with the idea of the Markov chain [10] .The concept of the Malkov chain, which is used to describe the process by which the state of a random sequence is transferred from one iteration to the next, was first proposed by the Russian mathematician Andrei Markov [1] .The Markov chain was the stepping stone that led to the development of diffusion-generation models in the years that followed.…”

Section: Historical Evolution Of the Generate Modelmentioning

confidence: 99%

“…The backwards process is also a Malkov chain [9] , and its combined probability distribution can be estimated by using the known forward probability transfer distribution in combination with the unknown backwards probability transmission distribution. A conditional probability distribution is used to estimate the reverse probability transfer distribution in a diffusion mode [10] , and it is assumed that this distribution follows the normal distribution. After training a neural network to obtain the correct parameters, an original image can be reconstructed based on the probability distribution of known conditions.…”

Section: Comparison Of Diffusion Modelsmentioning

confidence: 99%

Applications of Diffusion Model Image Restoration in the Field of Heritage Restoration: Overview and Outlook

2023

Frontiers in Artificial Intelligence and Applications

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The restoration of museum heritage is an important task with significant cultural and historical value; however, traditional methods of restoration are frequently constrained by the extent of the damage to the heritage as well as the constraints of the restoration techniques. In recent years, a method of restoration known as the diffusion model, which is based on computer vision and machine learning, has been gradually applied to the field of museum relics restoration and has shown enormous potential in relic restoration. This method of restoration was developed in the 1980s and is still in use today. In the field of image restoration, research and application of diffusion models are reviewed, and this article provides a summary of the development history, methodology principles, and application cases associated with these topics. This article provides an introduction to the fundamental ideas and principles underlying diffuse models, as well as a summary of the current state of research and an outlook on potential future trends and prospects pertaining to image repair within the domain of image processing.

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AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion

Zhang

2024

Heritage

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This study is concerned with the restoration of painted pottery images from the Yangshao period. The objective is to enhance the efficiency and accuracy of the restoration process for complex pottery patterns. Conventional restoration techniques encounter difficulties in accurately and efficiently reconstructing intricate designs. To address this issue, the study proposes an AI-assisted restoration workflow that combines Stable Diffusion models (SD) with Low-Rank Adaptation (LoRA) technology. By training a LoRA model on a dataset of typical Yangshao painted pottery patterns and integrating image inpainting techniques, the accuracy and efficiency of the restoration process are enhanced. The results demonstrate that this method provides an effective restoration tool while maintaining consistency with the original artistic style, supporting the digital preservation of cultural heritage. This approach also offers archaeologists flexible restoration options, promoting the broader application and preservation of cultural heritage.

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Research into Digital Oil Painting Restoration Algorithm Based on Image Acquisition Technology

Cited by 3 publications

References 4 publications

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Applications of Diffusion Model Image Restoration in the Field of Heritage Restoration: Overview and Outlook

AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion

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