Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction
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Abstract (arXiv)
The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisitions and add photon noise to the projection data. The prior is obtained by training a gradient-step denoiser using reconstructed simulated acquisitions. The trained model is integrated into a plug-and-play gradient-step algorithm to reconstruct images from simulated projections. Experiments on synthetic data demonstrate the denoising capabilities of the trained model, while qualitative evaluations on real images showcase the algorithm's performance and generalization ability.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 27 May 2026.
Builds on
10 of 16 references.
References · 10
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
- 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
- 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
- 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
- 2022Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imagingcited by 94
- 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
- 2020Learning Maximally Monotone Operators for Image Recoverycited by 62
- 2025DeepInverse: A Python package for solving imaging inverse problems with deep learningcited by 27
- 2023A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoisercited by 12
- 2025An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of Viewcited by 2
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Citing papers per year: 2026: 1.
1 paper.