A large open access dataset of transillumination imaging toward the realization of optical computed tomography
Abstract Transillumination imaging is commonly used in medicine and biometrics to provide non-invasive insights into internal structures. However, the prevalent image blurring resulting from scattering effects poses a significant challenge to the effective application of transillumination. Artificia...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04626-4 |
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| author | To Ni Phan Van Hoang Nhut Huynh Ngoc An Dang Nguyen Trung Nghia Tran Koichi Shimizu |
| author_facet | To Ni Phan Van Hoang Nhut Huynh Ngoc An Dang Nguyen Trung Nghia Tran Koichi Shimizu |
| author_sort | To Ni Phan Van |
| collection | DOAJ |
| description | Abstract Transillumination imaging is commonly used in medicine and biometrics to provide non-invasive insights into internal structures. However, the prevalent image blurring resulting from scattering effects poses a significant challenge to the effective application of transillumination. Artificial intelligence algorithms have gained prominence for enhancing transillumination images and addressing challenges such as scattering suppression, depth estimation, and three-dimensional structure reconstruction. These advances require high-quality input images to optimize model performance. Acquiring a large-scale transillumination image dataset for practical AI applications is difficult due to subjective imaging conditions. This study aimed to overcome this obstacle by introducing a comprehensive dataset of transillumination images. Methods and algorithms for generating depth-dependent point-spread function and transillumination images were presented. The dataset comprised 12,000 pairs of images of clear and scattered media, each associated with the corresponding depth information. This study is valuable for advancing AI-based solutions in transillumination imaging and provides a foundation for further research on image deblurring, depth perception, and 3D reconstruction. |
| format | Article |
| id | doaj-art-ce2377223a6041e89a43e9ceeb3db713 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-ce2377223a6041e89a43e9ceeb3db7132025-08-20T02:51:27ZengNature PortfolioScientific Data2052-44632025-03-0112111210.1038/s41597-025-04626-4A large open access dataset of transillumination imaging toward the realization of optical computed tomographyTo Ni Phan Van0Hoang Nhut Huynh1Ngoc An Dang Nguyen2Trung Nghia Tran3Koichi Shimizu4Graduate School of Information, Production and Systems, Waseda UniversityLaboratory of Laser Technology, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Laboratory of Laser Technology, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Laboratory of Laser Technology, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Graduate School of Information, Production and Systems, Waseda UniversityAbstract Transillumination imaging is commonly used in medicine and biometrics to provide non-invasive insights into internal structures. However, the prevalent image blurring resulting from scattering effects poses a significant challenge to the effective application of transillumination. Artificial intelligence algorithms have gained prominence for enhancing transillumination images and addressing challenges such as scattering suppression, depth estimation, and three-dimensional structure reconstruction. These advances require high-quality input images to optimize model performance. Acquiring a large-scale transillumination image dataset for practical AI applications is difficult due to subjective imaging conditions. This study aimed to overcome this obstacle by introducing a comprehensive dataset of transillumination images. Methods and algorithms for generating depth-dependent point-spread function and transillumination images were presented. The dataset comprised 12,000 pairs of images of clear and scattered media, each associated with the corresponding depth information. This study is valuable for advancing AI-based solutions in transillumination imaging and provides a foundation for further research on image deblurring, depth perception, and 3D reconstruction.https://doi.org/10.1038/s41597-025-04626-4 |
| spellingShingle | To Ni Phan Van Hoang Nhut Huynh Ngoc An Dang Nguyen Trung Nghia Tran Koichi Shimizu A large open access dataset of transillumination imaging toward the realization of optical computed tomography Scientific Data |
| title | A large open access dataset of transillumination imaging toward the realization of optical computed tomography |
| title_full | A large open access dataset of transillumination imaging toward the realization of optical computed tomography |
| title_fullStr | A large open access dataset of transillumination imaging toward the realization of optical computed tomography |
| title_full_unstemmed | A large open access dataset of transillumination imaging toward the realization of optical computed tomography |
| title_short | A large open access dataset of transillumination imaging toward the realization of optical computed tomography |
| title_sort | large open access dataset of transillumination imaging toward the realization of optical computed tomography |
| url | https://doi.org/10.1038/s41597-025-04626-4 |
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