A non rigid registration model for solar photosphere images based on a hybrid cross attention mechanism
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Graphical Abstract
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Abstract
The registration between solar photosphere image sequences is crucial for measured solar physics studies with high spatial and temporal resolutions. In previous studies, we found that the high-resolution photosphere images observed by ground-based telescopes still have some large-scale non-rigid distortions after high-resolution reconstruction, which cannot be eliminated by conventional sequence correlation alignment. These distortions will affect the study of small-scale fine activity in the solar photosphere. To solve this problem, we propose an image registration method based on deep learning in this paper(HCAM-Net). The method adopts an encoder-decoder framework, and introduces a hybrid attention mechanism to enhance the capture ability of context information, so as to obtain accurate deformation fields and realize non-rigid registration between images. The experimental results on the photosphere images observed by NVST show that the proposed method can achieve accurate non-rigid registration. Compared with the state-of-the-art methods, VoxelMorph and TransMorp, the quantitative indicators and the visualize results suggest the proposed method outperforms the other network in non-rigid registration of solar photosphere images. The SSIM index reaches 0.965 and R² score is 0.976.
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