Herein, for low-illumination application scenes, an end-to-end convolutional neural network (CNN) is proposed for the fusion of near-infrared (NIR) and visible images. The fused image can combine the signal-to-noise ratio of an NIR image and the color of visible images. To verify the capability of CNN for practical fusion tasks, a real dataset with accurate registration was collected. Moreover, the training set was preprocessed via information fusion, thereby enabling the network to extract additional information from NIR images. Experimental results reveal that the proposed method is superior to existing fusion methods in terms of visual quality and quantitative measurements.