Virtual restoration of paintings based on deep learning

Sizyakin, Roman; Voronin, Viacheslav; Pižurica, Aleksandra

doi:10.1117/12.2624371

Cited by 6 publications

(5 citation statements)

References 22 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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“…However, many artworks are already on the verge of collapse and thus need scientific exploration to reconstruct their history. Though several DL approaches are useful [77] for small test cases, significant problems remain in the darker region, which needs to be explored soon.…”

Section: Other Digital Restoration Techniques a Genetic Algorithmmentioning

confidence: 99%

“…Followed by the architectural differences in BIM, a plethora of Virtual Reality systems is investigated using IOT sensors and scanners [74], [75], [76]. We conclude our discussion with more aspects of revitalizing the heritage of popular architectures using various probabilistic methods, IOT-based AI techniques, and genetic algorithm [77], [78]. We show the materialized importance of heritage restoration tasks to VOLUME 11, 2023 FIGURE 3.…”

Section: Introductionmentioning

confidence: 96%

Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey

Basu

Paul²,

Ghosh

et al. 2023

IEEE Access

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Digitized methodologies in the recent era contribute to various fields of automation that used to hold different interests and meanings of human life. Buildings with historical significance, cultural values, and beliefs are becoming an interdisciplinary field of interest, engaging more computer scientists nowadays. Such structures need more attention towards reconstructing their values using a flavor of computerized tools instead of brickwork directly. Due to the wear of time, the tiles and engravings of most of the historical monuments are on the verge of ruin, endangering significant historical values. In this survey, we rebuild the values by delving deep into the device and methodologies by providing a comprehensive understanding of emerging fields and some experimental decisions. We discuss heritage restoration from some essential papers on 3D reconstruction, image inpainting, IoT-based methods, genetic algorithms, and image processing. The survey explains Machine Learning, Deep Learning, and Computer Vision-based methods for various restoration tasks in the related field. We divide this into certain parts contributing to different fields that restore cultural heritage. Moreover, we infer that the techniques will be faster, cheaper, and more beneficial to the context of image reconstruction in the near future.

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“…The current state-of-the-art crack detection methods are mostly based on deep learning. While the early results reported in the 2018 IP4AI meeting [35] were obtained using traditional CNN models, the current ones typically make use of U-Nets to allow more precise crack localization [32]. Another beautiful example of how crack detection can support the conservation treatment was reported in [31].…”

Section: A Crack Detection and Crack Pattern Analysismentioning

confidence: 99%

Revealing and Reconstructing Hidden or Lost Features in Art Investigation

Sober

Bucklow

Daly

et al. 2022

IEEE BITS Inform. Theory Mag.

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In recent decades, cultural heritage research -and in particular art investigation -has been undergoing a digital revolution. This is due both to improvements in the digitization and the acquisition of artefact's images generated using traditional 2D imaging methods as well as the growing adoption of a range of more recently introduced spectroscopic imaging techniques.A number of these imaging modalities use wavelengths of electromagnetic radiation that can penetrate surface layers thus yielding information from hidden features non-invasively. Different techniques are often used in combination to provide evidence of construction, condition and past treatment. These can also be used to characterize the materials used, how they were combined and map their distribution, giving insight into an artist's working method and the means to understand changes that have occurred over time. This wealth of data calls for the development of algorithmic approaches in order to handle and fully explore and interpret it. The questions one seeks B. Sober is with the

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Virtual restoration of paintings based on deep learning

Cited by 6 publications

References 22 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

Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey

Revealing and Reconstructing Hidden or Lost Features in Art Investigation

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