Infrared images are usually subject to low contrast, edge blurring and a large amount of noise. Aiming at improving the quality of the infrared images, this paper presents a novel adaptive algorithm on infrared image enhancement. Firstly, the input image is decomposed via the nonsubsampled Contourlet transform (NSCT) to achieve the coefficients of subbands at different scales and directions. Next, the coefficients of high frequency are classified into three categories automatically by using an adaptive classification method which analyzes the coefficients in their local neighborhood. After that, a nonlinear mapping function is adopted to modify the coefficients, in order to highlight the edges and suppress the high frequency noise. Finally, the enhanced image is obtained by reconstructing via the above modified coefficients. Experiment results show that the proposed algorithm could effectively enhance image contrast and highlight edges while avoiding the image distortion and noise amplification.