Deformable Image Registration with Multi-scale Feature Fusion from Shared Encoder, Auxiliary and Pyramid Decoders
This work addresses image registration for medical or computer vision applications, but it appears incremental as it builds on existing pyramid networks with added decoders and fusion blocks.
The paper tackled the problem of deformable image registration by enhancing a pyramid network with a shared auxiliary decoder and multi-scale feature fusion, achieving higher registration accuracy and smooth deformations.
In this work, we propose a novel deformable convolutional pyramid network for unsupervised image registration. Specifically, the proposed network enhances the traditional pyramid network by adding an additional shared auxiliary decoder for image pairs. This decoder provides multi-scale high-level feature information from unblended image pairs for the registration task. During the registration process, we also design a multi-scale feature fusion block to extract the most beneficial features for the registration task from both global and local contexts. Validation results indicate that this method can capture complex deformations while achieving higher registration accuracy and maintaining smooth and plausible deformations.