To improve the quality of multi-focus image fusion, a fully convolutional neural network multi-focus image fusion algorithm based on supervised learning is proposed. The proposed algorithm aims to use neural networks to learn the complementary relationship between different focus areas of the source image, that is, to select different focus positions of the source image to synthesize a global clear image. In this algorithm, the focus images are constructed as training data, and the dense connection and 1×1 convolution are used in the network to improve the understanding ability and efficiency of the network. The experimental results show that the proposed algorithm is superior to other contrast algorithms in both subjective visual evaluation and objective evaluation, and the quality of image fusion is significantly improved.