In order to solve the problems of unclear contours of heat source objects and missing image content in severely exposed regions when visible and infrared images are fused by using basic Laplacian blending， an image fusion algorithm that preserves infrared contours and gradient information was proposed. Firstly， the input image was transformed into color space and denoised by adaptive morphology， and the gradient contrast of the two images and the contour of the highlighted object in the infrared image were taken as the weights of pixel activity information. Secondly， the weights and the input images were decomposed simultaneously， and the weight assignment was adjusted by similarity-based comparison. Finally， the image was reconstructed and the color space was transformed. In subjective evaluation， the proposed algorithm does not produce artifacts and strange colors， and the contours of the heat object in the obtained image is clear. In objective evaluation， the proposed algorithm has an ENtropy （EN） of 7.49， an Edge Intensity （EI） of 74.61， and an Average Gradient （AG） of 7.23， compared with the traditional multi-scale transformation methods （including Non-Subsampled Contourlet Transformation （NSCT） method， the method based on Non-Subsampled Shearlet Transform （NSST） multi-scale entropy） and the latest deep learning method （such as the method combining Residual Network （ResNet） and Zero-phase Component Analysis （ZCA））， it improves EN by 0.10， 0.58 and 0.75， EI by 6.65， 20.35 and 37.35， and AG by 0.73， 2.19 and 3.55； it also achieves a processing speed of 5 frame/s on Intel i5 series computers with low computational complexity.