Super-Resolution Surface Reconstruction from Few Low-Resolution Slices

• URL: http://arxiv.org/abs/2309.05071v2
• Date: Tue, 12 Sep 2023 18:28:40 GMT
• Title: Super-Resolution Surface Reconstruction from Few Low-Resolution Slices
• Authors: Yiyao Zhang, Ke Chen and Shang-Hua Yang
• Abstract summary: This paper proposes a new variational model for increasing the resolution of segmented surfaces in numerical simulations. We implement two numerical algorithms for solving the model, a projected gradient descent method and the alternating direction method of multipliers. The advantages of the new model are shown through quantitative comparisons by the standard deviation of Gaussian curvatures and mean curvatures from the viewpoint of discrete geometry.
• Score: 7.053276723038573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many imaging applications where segmented features (e.g. blood vessels) are further used for other numerical simulations (e.g. finite element analysis), the obtained surfaces do not have fine resolutions suitable for the task. Increasing the resolution of such surfaces becomes crucial. This paper proposes a new variational model for solving this problem, based on an Euler-Elastica-based regulariser. Further, we propose and implement two numerical algorithms for solving the model, a projected gradient descent method and the alternating direction method of multipliers. Numerical experiments using real-life examples (including two from outputs of another variational model) have been illustrated for effectiveness. The advantages of the new model are shown through quantitative comparisons by the standard deviation of Gaussian curvatures and mean curvatures from the viewpoint of discrete geometry.

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