Traditional methods cannot fully mine image-focus association information, thereby leading to the distortion of fusion details. In this study, a multi-focus image fusion method based on collaborative detection via densely connected convolutional neural networks is proposed to address this issue. Multi-focused source images are integrated to detect focused features collaboratively, and the features of deep dense convolutional networks, such as feature reuse, and the combination of low-level and high-level features are used to enhance the multi-focused image feature representation, which better mine the images' semantic information. By leveraging feature reuse, multi-focus source images are integrated to achieve collaborative focus feature detection. The multi-scale pyramid pooling strategy is used to aggregate the global context information of different focus regions to enhance the ability to distinguish the focused and defocused areas and obtain a rough fusion-probability decision graph. Furthermore, a convolution conditional random field (CRF) is adopted to optimize the decision graph and the refined probabilistic decision graph is obtained. Finally, the fused image is obtained with its details preserved. A pair of multi-focused images are combined into six channels and fed into the network for training, thus ensuring that the focused areas are correlated. The proposed method is evaluated subjectively and objectively using public data sets. The experimental results show that the proposed method produces effective fusion and fully mines the focused association information and retains sufficient image detail.