Haihui WuThis email address is being protected from spambots. You need JavaScript enabled to view it.
Liaoning National Normal College, Fuxin, 123000, China
Received: June 8, 2025 Accepted: September 18, 2025 Publication Date: October 18, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
In multi-focus image fusion tasks, traditional methods have the problem of uneven processing between focusing and defocusing boundary regions. To address the above problem, this paper proposes a novel multi-focus image fusion based on a Transformer and weighted guided filtering. In this paper, the generative adversarial network is the backbone network. The generator performs an end-to-end multi-focus image fusion task. The Transformer is used to obtain global dependencies and low-frequency spatial details. Then it uses a cross-domain cross attention mechanism to help generate the double branches to achieve the effect of information interaction, and obtains redundant information and complementary information. An improved guided filtering algorithm is used to fuse CbCr channels after RGB color space conversion. The experimental results show that the proposed algorithm is superior to other algorithms in both subjective visual evaluation and objective evaluation, and the image fusion quality is further improved.
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