In order to solve the problem of low contrast and fuzzy details of infrared image， an infrared image enhancement method based on multi-scale convolution combined with adaptive bi-interval luminance equalization is proposed. Firstly， the image is pre-processed by multi-scale convolution； Then， the threshold of image segmentation is solved by genetic algorithm， where the function of maximizing intra class variance and minimizing inter class variance is taken as its fitness function， the double interval histogram with detail information is used to equalize， and the brightness of image is improved by introducing the gray level homogenization of mean square and mean square. Finally， the image with clear details and strong contrast is reconstructed by linear weighted fusion of the detail image extracted by adaptive limited Laplace and the image with brightness enhancement. Compared with conventional methods in the infrared images of different scenes and gray images with abundant details to verify the validity of the proposed method， the maximum growth rates of En， EME and AG in images processed by this method increased from 5.039 1， 13.446 1 and 7.845 0 to 7.163 3， 90.252 5 and 53.617 7， respectively. The experimental results show that this method has better performance.