Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908691348848640 |
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| author | Bell, Evan Liang, Shijun Alkhouri, Ismail Ravishankar, Saiprasad |
| author_facet | Bell, Evan Liang, Shijun Alkhouri, Ismail Ravishankar, Saiprasad |
| contents | Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstruction problems. In this work, we introduce Tada-DIP, a highly effective and fully 3D DIP method for solving 3D inverse problems. By combining input-adaptation and denoising regularization, Tada-DIP produces high-quality 3D reconstructions while avoiding the overfitting phenomenon that is common in DIP. Experiments on sparse-view X-ray computed tomography reconstruction validate the effectiveness of the proposed method, demonstrating that Tada-DIP produces much better reconstructions than training-data-free baselines and achieves reconstruction performance on par with a supervised network trained using a large dataset with fully-sampled volumes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_03962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction Bell, Evan Liang, Shijun Alkhouri, Ismail Ravishankar, Saiprasad Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstruction problems. In this work, we introduce Tada-DIP, a highly effective and fully 3D DIP method for solving 3D inverse problems. By combining input-adaptation and denoising regularization, Tada-DIP produces high-quality 3D reconstructions while avoiding the overfitting phenomenon that is common in DIP. Experiments on sparse-view X-ray computed tomography reconstruction validate the effectiveness of the proposed method, demonstrating that Tada-DIP produces much better reconstructions than training-data-free baselines and achieves reconstruction performance on par with a supervised network trained using a large dataset with fully-sampled volumes. |
| title | Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2512.03962 |