Current infrared and visible image fusion algorithms often suffer from issues such as unclear texture details in the fused image and an unbalanced display of infrared information and texture details.In this paper,we propose an image fusion method of pseudo-color infrared and visible images based on attention-dense network.The greyscale infrared image is first processed in pseudo-color and then combined with the colored visible image to form a multi-channel data input fusion network.Secondly,a generator network structure consisting of convolutional layers and densely connected blocks with attention modules is designed to focus on the key information of the source image and enhance the ability of the network to extract information from the source image.Finally,the content loss function is constructed by using infrared pixels,visible pixels,visible gradient and infrared gradient to keep the stability of infrared target and texture details in the fused image.Qualitative and quantitative comparisons are made with five representative fusion methods. The results show that the peak signal-to-noise ratio,information entropy,average gradient,and mutual information of the fused images obtained by this method achieve the optimal values of 31.6841,6.5581,6.0096,and 3.0960, respectively.The quantitative and qualitative results demonstrate that the proposed fusion method results in a fused image with richer texture details and good visual effects.