Infrared and visible image fusion based on multi‐channel convolutional neural network
Hongmei Wang, Wenbo An, Lin Li, Chenkai Li, Daming Zhou
Abstract
Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics.