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arXiv · 2512.03962

Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction

Evan Bell·Shijun Liang·Ismail Alkhouri·Saiprasad Ravishankar
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Abstract

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.

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Evan Bell, Shijun Liang, Ismail Alkhouri, Saiprasad Ravishankar. 2025-12-03. Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction. https://arxiv.org/abs/2512.03962

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