Reconstructing Artistic Heritage: Style Transfer in Ottoman Miniature Paintings Using Pre-Trained CNN with PSNR Based Image Similarity
| dc.contributor.author | Altundogan T.G. | |
| dc.contributor.author | Karakose M. | |
| dc.date.accessioned | 2025-04-10T11:02:43Z | |
| dc.date.available | 2025-04-10T11:02:43Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Deep learning techniques, inspired by the workings of the human brain, are enhancing the performance of many productive computer science applications in the field of culture and art. In this study, pre-trained CNNs were used to transfer styles on works of art that have a unique style, such as Ottoman miniatures. The styles of different Ottoman miniatures were transferred to other paintings that are not works of art or works of art with this method. The new images obtained were evaluated using PSNR and SSIM metrics to be compared with similar studies in the literature. The results showed that this study performed in parallel with other similar studies. This study presents a potential method that can contribute to research in the field of transferring styles on works of art such as Otto man miniatures. © 2024 IEEE. | |
| dc.identifier.DOI-ID | 10.1109/ICSH62408.2024.10779766 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/44231 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.title | Reconstructing Artistic Heritage: Style Transfer in Ottoman Miniature Paintings Using Pre-Trained CNN with PSNR Based Image Similarity | |
| dc.type | Conference paper |