Reproduce “Disentangle-then-Align: Non-Iterative Hybrid Multimodal Image Registration via Cross-Scale Feature Disentanglement” on OpenResearch
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About this paper
Chunlei Zhang, Jiahao Xia, Yun Xiao, Bo Jiang, Jian Zhang
HRNet, a unified non-iterative framework, combines cross-scale feature disentanglement with hybrid transformation prediction for multimodal image registration. It simultaneously estimates global rigid and local non-rigid deformations, achieving state-of-the-art performance across four diverse datasets, including an average relative reprojection error reduction of approximately 72% for rigid tasks.
Key insights
- Modality-Specific Batch Normalization effectively mitigates cross-modal statistical shifts, enhancing the shared backbone's ability to learn geometric commonalities.
- Explicitly preventing modality-private information leakage and adaptively projecting shared features into a stable, low-dimensional subspace is crucial for learning robust geometric representations.
- A unified, non-iterative pipeline that jointly estimates rigid and non-rigid transformations within the same shared feature space overcomes the limitations of sequential or decoupled hybrid registration methods.
Summary by alphaXiv.
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