Multi-focus image fusion based on unsupervised learning

Kaijun Wu, Yuan Mei

Abstract: In the multi-focus image fusion task, how to better balance the clear region information of the original image with different focus positions is the key. In this paper, a multi-focus image fusion model based on unsupervised learning is designed, and the image fusion task is carried out by two-stage processing. In the training phase, the encoder–decoder structure is adopted and the multi-scale structural similarity is introduced as the loss function for image reconstruction. In the fusion stage, the trained encoder is used to encode the feature of the original image. The spatial frequency is used to distinguish the clear area of the image from the two scales of channel and space, and the pixels with inconsistent discrimination are checked and processed to generate the initial decision diagram. The final image fusion task is carried out after mathematical morphology optimization. The experimental results show that this method has good effect on preserving the texture details and edge information of the focused area of the original image. Compared with the five advanced fusion algorithms, the proposed algorithm has achieved preferential fusion performance.