Abstract: The image fusion method based on residual network and dense network has the problem of losing some useful information in the middle layer of network and unclear details of fusion image.Therefore,a multi-modal medical image fusion based on the Dual Residual Hyper-Densely Networks (DRHDNs) is proposed.DRHDNs is divided into two parts:feature extraction and feature fusion.In the feature extraction part,a dual residual hyper dense blocks is constructed by combining hyper dense connection and residual learning.The hyper dense connection not only occurs between layers in the same path,but also between layers in different paths.This connection makes the feature more sufficient,the detail information more abundant,and the initial feature fusion of the source image is carried out .Feature fusion part is for final fusion.Compared with the other six image fusion methods,four groups of brain images are fused and compared,and an objective comparison is made from four evaluation indexes.Results show that DRHDNs has good performance in detail retention and contrast.The fusion image has rich and clear detail information,which conforms to human visual.

Key words: Convolutional neural network, Dual residual learning, Hyper dense connection, Medical image fusion, Multi-modal