Multi-focus image fusion with visual state space model and dual adversarial learning

In recent years, the two-stage multi-focus image fusion (MFF) method, which utilizes neural networks to first generate decision maps and then calculate the fused image, has witnessed significant advancements. However, after supervised training, many networks become overly reliant on semantic information, making it challenging to discern whether homogeneous regions and flat regions are in focus or not, as these regions lack distinct blur cues. To alleviate this issue, this paper proposes a multi-focus image fusion network named BridgeMFF by applying a visual state space model and developing a general fine-tuning technique named BridgeTune, which bridges the semantic and texture gap via dual adversarial learning. By fine-tuning the entire network in an adversarial manner, decision maps are generated to synthesize clear and blurred images with probability density distributions closely approximating real ones, thereby implicitly learning local spatial patterns and statistical properties of pixel values. Extensive experiments demonstrate that the proposed BridgeMFF achieves superior fusion quality, especially in challenging homogeneous regions. Moreover, BridgeMFF has the smallest model size (0.05M) and fastest processing speed (0.09s per image pair), enabling real-time fusion applications. The codes are available at https://github.com/Xinzhe99/BridgeMFF.