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

• URL: http://arxiv.org/abs/2512.03962v1
• Date: Wed, 03 Dec 2025 16:56:38 GMT
• Title: Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
• Authors: Evan Bell, Shijun Liang, Ismail Alkhouri, Saiprasad Ravishankar,
• Abstract summary: We introduce Tada-DIP, a highly effective and fully 3D DIP method for solving 3D inverse problems.<n>By combining input-adaptation and denoising regularization, Tada-DIP produces high-quality 3D reconstructions.<n>Experiments on sparse-view X-ray computed tomography reconstruction validate the effectiveness of the proposed method.
• Score: 14.275526906868622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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