In order to improve the details of fusion image from infrared and visible image and reduce artifacts and noise, an infrared and visible image fusion algorithm based on ResNet152 deep learning model is proposed. Firstly, the source image is decomposed into the low frequency part and the high frequency part. The low frequency part is fused by the average weighting strategy to put a new low frequency part. The high frequency part is extracted by ResNet152 to obtain multiple feature layers.The L1 regularization, convolution operation, bilinear interpolation upsampling, and maximum selection strategy for the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high frequency part to obtain a new high frequency part. Finally, the image is reconstructed by the new low frequency part and high frequency part. Experimental results show that the proposed algorithm can get more texture information while retaining the significant features of the image, and effectively reduces artifacts and noise. The subjective evaluation and objective evaluation are better than the comparison algorithm.