A Hybrid Supervised Fusion Deep Learning Framework for Microscope Multi-Focus Images

The quality of multi-focus microscopic image fusion hinges upon the precision of the image registration technology. However, algorithms for registration tailored specifically for multifocal microscopic images are lacking. Due to the presence of fuzzy regions and weak textures of multi-focus microscope images, the registration of patches is suboptimal. For these problems, this paper formulates a hybrid supervised deep learning model. It can improve the accuracy of registration and fusion. The generalization ability of the model to the actual deformation field enhance by the artificial deformation field. A step of patch movement simulation is employed to blur the multi-focus microscopic images and make synthetic flow, thus emulating distinct fuzzy regions in the two images to be registered, consequently enhancing the model's generalization ability. The experiments demonstrate that our proposed approach is superior to the existing registration algorithms and improves the accuracy of image fusion.