Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fields
Many imaging problems require computing spatial transformations induced by spatially varying intensity, feature, or density fields. Canonical examples include distortion correction, deformable image registration, atlas-based segmentation, and deformation-driven image analysis. These tasks can be formulated as geometric mapping problems in which the transformation is constrained to preserve local structure, control boundary behavior, or regulate angular distortion. Such formulations typically lead to variational models, diffusion processes, or elliptic partial differential equations. However, repeatedly solving high-resolution systems becomes computationally expensive when the underlying p...
| Authors | Yanwen Huang, Lok Ming Lui, Gary P. T. Choi |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 28 May 2026 |
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