Multifocus image fusion based on hybrid feature extraction and interactive fusion

Multi-focus image fusion is the process of synthesizing multiple images with different focal points into a single image with higher clarity and a wider depth of field. However, existing methods face challenges when dealing with complex scenes, such as information loss, insufficient extraction of image details, and inaccurate fusion. Therefore, this paper proposes a multi-focus image fusion method based on hybrid feature extraction and interactive fusion. By enhancing the network's ability to capture image details through mixed extraction blocks, feature information is effectively extracted. Subsequently, interactive fusion blocks enhance the interaction between features from different sources, making feature fusion more natural and effective. Finally, a feature reconstruction decoder is designed using convolutional layers and reverse bottleneck structures to effectively avoid information loss and maintain the richness and integrity of features. Extensive experiments indicate that, compared to 10 other state-of-the-art methods, the proposed approach in this paper exhibits superior performance in multi-focus image fusion tasks.