In order to further improving the performance of infrared and visible image fusion method,an infrared and visible image fusion method based on multiscale local extrema decomposition (MLED) and deep learning network ResNet152 is proposed in this paper.Firstly,the source images are decomposed into approximate images and many detail images using MLED,which can separate out the overlapped important feature information.Secondly,the residual network ResNet152 is used to extract the multi-dimensional deep features of the source images,and the ｌ1-norm is used as the activity level measure to generate the salient feature maps,the weighted average fusion algorithm is carried out for the approximate images,which can keep the energy and residual details not lost.For the detail images,the initial decision map is obtained by the rule “coefficient absolute max”.The source images are used as the guided images,and the initial decision maps are used as the input images for guided filtering.So the optimized decision maps are obtained,the weighted local energy is calculated to get the energy saliency maps.The weighted average algorithm is carried out for the detail images,which can make the fusion image having rich texture details and good visual edge perception.Finally,the fusion image is obtained by reconstructing the fused approximate image and detail images.The experimental results show that,compared with the existing typical fusion methods,the proposed method can achieve state-of-the-art results in terms of both objective evaluation and visual quality.