Metal Artifact Reduction Methods Using Deep Generative Models for Cultural Relics X-ray CT Images
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Computerized tomography (CT) provides non-invasive visualization of internal structural information without losing any detail. It has proven to be very useful in protecting cultural relics. However, metal cultural relics are frequently accompanied by destructive metal artifacts in x-ray CT images, making it impossible for traditional methods to obtain detailed information from the cultural relics. In recent years, deep generative-based models have demonstrated great promise for solving such problems. However, due to the complicated structure and diverse materials of cultural relics, it is difficult to accurately restore the highly heterogeneous details of cultural relics in practical applications. As a result, the primary focus of this study is on the removal of metal items from cultural relics using deep generative models and achieves effective restoration of highly heterogeneous details by combining mask-guided strategies. Specifically, we first collaborated with the Palace Museum to build a cultural relic CT dataset and specifically divided artifacts into two categories: sharp edge smoothing and edge distortion according to their complexity. Second, many deep generative networks include CycleGAN, CSGAN, MUNIT, DeblurGAN, and DRIT were trained. Additionally, segmentation masks were blended to create artifact-free images. Finally, the performance was enhanced via dynamic weight adjustment. The effectiveness has been qualitatively and quantitatively validated on the cultural relic CT dataset. PSNR and SSIM metrics confirm the model’s ability to restore fine details, providing reliable support for future cultural relic protection.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Metal Artifact Reduction Methods Using Deep Generative Models for Cultural Relics X-ray CT Images
- Date Crossref
- 08/07/2025
- Éditeur
- IEEE
- Type
- proceedings-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Beijing University of Technology pays non établi dans la noticeUniversité ou école supérieure
-
Palace Museum pays non établi dans la noticeInstitution
-
Yunnan Archaeology pays non établi dans la noticeStructure de recherche
-
Henan Provincial Institute of Cultural Heritage and Archaeology pays non établi dans la noticeInstitution
-
Beijing Information Science & Technology University pays non établi dans la noticeUniversité ou école supérieure
-
University of Science and Technology Beijing pays non établi dans la noticeUniversité ou école supérieure
-
Beijing International Studies University pays non établi dans la noticeUniversité ou école supérieure
-
Sichuan Provincial Institute of Cultural Relics and Archaeology pays non établi dans la noticeStructure de recherche
Beijing University of Technology, Palace Museum et Yunnan Archaeology, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.