To improve the quality of multi-focus image fusion in photography applications, a multi-focus image fusion algorithm based on supervised learning for fully convolutional network is proposed. The aim of this algorithm is to make the neural network learn the complementary relationship between different focusing areas of source images, which is to select different focusing positions of the source images to synthesize a global clear image. In this algorithm, focusing images are constructed as training data. Dense connection and 1 × 1 convolution are used in the network to improve the understanding ability and efficiency of the network. The result of experiment shows 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. Code is available at https://github.com/littlebaba/SF_MFIF.

为了提高摄影应用中多焦点图像融合的质量，提出了一种基于监督学习的全卷积网络多焦点图像融合算法。该算法的目的是使神经网络学习源图像不同聚焦区域之间的互补关系，即选择源图像的不同聚焦位置来合成全局清晰图像。在该算法中，聚焦图像被构造为训练数据。网络中使用密集连接和1×1卷积，以提高网络的理解能力和效率。实验结果表明，该算法在主观视觉评价和客观评价方面均优于其他对比算法，图像融合的质量大大提高。可以从https://github.com/littlebaba/SF_MFIF获得代码。