Double-view eye fundus image fusion method based on deep learning

The invention provides a double-view eye fundus image fusion method based on deep learning, and the method is characterized in that the method comprises the following steps: S1, carrying out the preprocessing of two to-be-detected images, and obtaining two preprocessed images; S2, building a convolutional neural network model, and training the convolutional neural network model to obtain a trained convolutional neural network model called M-net; S3, dividing the M-net into two parts, namely an M-net Part I and an M-net Part II; S4, respectively putting the two preprocessed images into M-net Part I for feature extraction, and obtaining two image feature maps; S5, splicing the two image feature maps to obtain a spliced image; S6, putting the spliced image into M-net Part II for feature fusion. 本发明提供了一种基于深度学习的双视野眼底图像融合方法，具有这样的特征，包括以下步骤，步骤S1，对两张待测图像进行预处理获得两张预处理图像；步骤S2，搭建卷积神经网络模型，对卷积神经网络模型进行训练，从而得到训练后的卷积神经网络模型，称为M-net；步骤S3，将M-net分成两部分，称为M-net PartⅠ和M-net PartⅡ；步骤S4，将两张预处理图像分别放入M-net PartⅠ进行特征提取，获得两张图像特征图；步骤S5，将两张图