Overview of Heterogeneous Image Registration Methods Based on Graph Neural Networks
This research aims to explore the application and performance of Graph Neural Network(GNN)in addressing heterogeneous image registration tasks,providing support for subsequent tasks such as image fusion or stitching.By reviewing existing literature,various GNN models and their applications in the field of image registration are introduced,and a comparison of different GNN architectures is conducted to evaluate the performance of each model.The research reveals that GNN models demonstrate superior performance in handling heterogeneous image registration tasks compared to traditional methods,leveraging their ability to effectively utilize graph structural information and finely capture node attribute information.This systematic study of image registration methods offers new technical insights for addressing the challenges of low accuracy and high difficulty in heterogeneous image registration tasks.
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