ARQGAN: An evaluation of generative adversarial network approaches for automatic virtual inpainting restoration of Greek temples

Nogales, Alberto; Delgado-Martos, Emilio; Melchor, Ángel; García‐Tejedor, Álvaro J.

doi:10.1016/j.eswa.2021.115092

Cited by 11 publications

(6 citation statements)

References 17 publications

(9 reference statements)

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“…Generative adversarial networks (GANs) are a combination of proposal and evaluation components of machine learning. They are frequently employed as approximative techniques in 3D modelling, e.g., for single photo digitization [140], completion of incomplete 3D digitized models [141,142] and photo-based reconstructions [143]. Regarding transparency, most current machine learning approaches work within black box settings [121,144].…”

Section: Machine Learning and Hybrid Methodsmentioning

confidence: 99%

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

Münster,

Maiwald,

Bruschke

et al. 2024

Applied Sciences

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Numerous digital media repositories have been set up during recent decades, each containing plenty of data about historic cityscapes. In contrast, digital 3D reconstructions of no longer extant cityscapes have so far almost solely been performed for specific cases and via manual modelling techniques. Within the Jena4D research group, we are investigating and developing methods and technologies for transferring historical media and their contextual information into 4D models. The next step will be to automate this process and extend it to the world scale. Our team are working on different facets of that endeavor: retrieving images, text, and object data from public sources; crowdsourcing and collecting qualitative data from contests and pupil labs; processing historical plans and photographs to extract spatial data; location-based mobile visualization; and collection-browsing interfaces. This article is intended to highlight (1) current challenges, (2) the state of the art, (3) our approach to 4D modelling and visualization on the world scale, and (4) results from testing demo cases in Dresden, Jena, and Amsterdam.

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Section: Machine Learning and Hybrid Methodsmentioning

confidence: 99%

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

Münster,

Maiwald,

Bruschke

et al. 2024

Applied Sciences

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“…Specifically generative adversarial networks (GAN) as a combination of proposal and assessment components of machine learning are frequently employed in 3D modelling. Application scenarios are single photo digitization [466], completion of incomplete 3D digitized models [467,468], or photo-based reconstructions [469].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Digital 3D Technologies for Humanities Research and Education: An Overview

Münster¹

2022

Applied Sciences

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Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods.

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“…Deepak Pathak et al [11] first proposed an unsupervised visual feature learning method based on contextual pixel prediction using a neural network (NN) approach, which laid the foundation for many subsequent approaches. Alberto Nogales et al [12] developed a deep-learning model based on GANs for the automatic digital reconstruction of Greek temples. The method automatically repairs Greek temples based on a rendering of the ruins obtained from the 3D model.…”

Section: Introductionmentioning

confidence: 99%

Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China

Sun

Hou

Lyu

et al. 2022

Sensors

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Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the original first principal component with a high-pass filtered first principal component image, which was then inverse PCA transformed with the other original principal component images to obtain an enhanced hyperspectral image. The linear information in the mural was therefore enhanced, and the differences between the scratches and background improved. Second, the enhanced hyperspectral image of the mural was synthesized as a true colour image and converted to the HSV colour space. The light brightness component of the image was estimated using the multi-scale Gaussian function and corrected with a 2D gamma function, thus solving the problem of localised darkness in the murals. Finally, the enhanced mural images were applied as input to the triplet domain translation network pretrained model. The local branches in the translation network perform overall noise smoothing and colour recovery of the mural, while the partial nonlocal block is used to extract the information from the scratches. The mapping process was learned in the hidden space for virtual removal of the scratches. In addition, we added a Butterworth high-pass filter at the end of the network to generate the final restoration result of the mural with a clearer visual effect and richer high-frequency information. We verified and validated these methods for murals in the Baoguang Hall of Qutan Temple. The results show that the proposed method outperforms the restoration results of the total variation (TV) model, curvature-driven diffusion (CDD) model, and Criminisi algorithm. Moreover, the proposed combined method produces better recovery results and improves the visual richness, readability, and artistic expression of the murals compared with direct recovery using a triple domain translation network.

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ARQGAN: An evaluation of generative adversarial network approaches for automatic virtual inpainting restoration of Greek temples

Cited by 11 publications

References 17 publications

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

Digital 3D Technologies for Humanities Research and Education: An Overview

Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China

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