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Multi-focus image fusion based on unsupervised learning

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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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61966022), the Natural Science Foundation of Gansu Province (21JR7RA300) and the Open Project of the Dunhuang Cultural Heritage Protection Research Center of Gansu Province (o.Gdw2021Yb15).

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  1. School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China

    Kaijun Wu & Yuan Mei

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  1. Kaijun Wu
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  2. Yuan Mei
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Correspondence to Yuan Mei.

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Wu, K., Mei, Y. Multi-focus image fusion based on unsupervised learning. Machine Vision and Applications 33, 75 (2022). https://doi.org/10.1007/s00138-022-01326-6

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