🤖 AI Summary
This work addresses the challenges in multimodal image registration, where modality-specific information often leaks into the shared feature space and existing methods struggle to jointly model global rigid alignment and local non-rigid deformations. To overcome these limitations, the authors propose HRNet, which employs a shared backbone enhanced with modality-specific batch normalization (MSBN) and introduces a cross-scale decoupling and adaptive projection module (CDAP) to effectively suppress modality interference. Furthermore, a hybrid parameter prediction module (HPPM) is designed to unify the prediction of rigid transformations and non-rigid deformation fields within an end-to-end, non-iterative framework. The proposed method achieves state-of-the-art performance in both rigid and non-rigid registration across four multimodal datasets.
📝 Abstract
Multimodal image registration is a fundamental task and a prerequisite for downstream cross-modal analysis. Despite recent progress in shared feature extraction and multi-scale architectures, two key limitations remain. First, some methods use disentanglement to learn shared features but mainly regularize the shared part, allowing modality-private cues to leak into the shared space. Second, most multi-scale frameworks support only a single transformation type, limiting their applicability when global misalignment and local deformation coexist. To address these issues, we formulate hybrid multimodal registration as jointly learning a stable shared feature space and a unified hybrid transformation. Based on this view, we propose HRNet, a Hybrid Registration Network that couples representation disentanglement with hybrid parameter prediction. A shared backbone with Modality-Specific Batch Normalization (MSBN) extracts multi-scale features, while a Cross-scale Disentanglement and Adaptive Projection (CDAP) module suppresses modality-private cues and projects shared features into a stable subspace for matching. Built on this shared space, a Hybrid Parameter Prediction Module (HPPM) performs non-iterative coarse-to-fine estimation of global rigid parameters and deformation fields, which are fused into a coherent deformation field. Extensive experiments on four multimodal datasets demonstrate state-of-the-art performance on rigid and non-rigid registration tasks. The code is available at the project website.