为进一步提高多聚焦图像的融合质量,提出一种基于监督学习的全卷积神经网络多聚焦图像融合算法。该算法旨在运用神经网络学习源图像不同聚焦区域的互补关系,即选择源图像中不同的聚焦位置合成一张全局清晰图像。该算法构造聚焦图像作为训练数据,网络采用稠密连接和1×1卷积以提高网络的理解能力和效率。实验结果表明,本文算法在主观视觉评估和客观评价两方面均优于其他对比算法,图像的融合质量得到进一步提升。

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.