A nonrigid registration deep-learning model for solar photosphere images using a hybrid cross-attention mechanism

Image registration within a solar photosphere sequence is crucial for observational solar physics studies requiring high spatial and temporal resolutions. Previously, we identified residual large-scale nonrigid distortions in high-resolution solar photosphere images from ground-based telescopes after high-resolution reconstruction. Because these distortions are not eliminated by conventional sequence correlation alignment, they can affect the analysis of small-scale activity in the solar photosphere. Here, we implemented an image registration model using deep learning (HCAM-Net) to solve the problem. Within an encoder–decoder framework, we introduced a hybrid attention mechanism to improve context information capture and extract accurate deformation fields. Analyzing solar photosphere images acquired by the New Vacuum Solar Telescope, we demonstrated that the proposed model effectively achieved highly accurate nonrigid image registration. Evaluation metrics and visualization results indicated that our model outperformed current state-of-the-art models, such as VoxelMorph and TransMorph, for nonrigid registration of solar photosphere images, with a structural similarity index measure of 0.965 and a coefficient of determination of 0.976.