At present, many infrared image enhancement methods use image gray features to enhance the image. These methods ignore the non-uniformity of image gray distribution, resulting in the loss of details and unsatisfactory contrast. In order to overcome the above problems, an infrared image enhancement algorithm based on adaptive histogram equalization coupled with Laplace transform is proposed. Firstly, after the input image is evenly segmented, the degree of non-uniformity of image gray distribution is calculated with the help of Gini coefficient of Lorentz curve. Based on the degree of non-uniformity of image gray distribution, the adaptive upper and lower thresholds are constructed to realize adaptive histogram equalization for image contrast enhancement. Then, the smooth filtering method is introduced to remove the noise in the image. Based on the traditional Laplace transform, the eight neighborhood Laplace transform is formed by integrating the diagonal second derivative information of image pixel value, which is used to sharpen the image edge and other details, so as to enhance the image clarity. Finally, the algorithm is used to enhance different infrared images. The experimental results show that the contrast and clarity of the enhanced image are better, and the visual effect is better.