Super-Resolution Surface Reconstruction from Few Low-Resolution Slices
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
토론
이 논문과 연결된 포럼 글이 아직 없습니다.