In order to overcome the problems of low contrast, serious lack of details and textures in traditional algorithms, and noise sensitivity, weak adaptability and anti-interference ability of saliency-based detection algorithms, a non-subsampled contourlet transform (NSCT) infrared and visible light fusion method based on improved frequency tuning (FT) saliency detection is proposed. Firstly, an improved saliency map extraction algorithm is used to target the infrared image to distinguish the target from the background. Secondly, the infrared image and visible light image are decomposed into high and low frequency subbands using NSCT. The coefficients are used to guide the fusion, which can well preserve the contrast between the target and the background. For the high-frequency part, the rule of local weighted energy is used to make a decision, the weighted least squares (WLS) optimization can obtain more detailed information and reduce the influence of noise, and the fused high-frequency and low-frequency subbands coefficients are processed. Finally, the fused image is obtained by inverse NSCT transform. The experimental comparison results of four sets of images show that the method in this paper has better visual effect than other methods in terms of prominent target, rich details extraction, and obvious elimination of edge artifacts, subjectively. It is in the best state on the average gradient (AG), information entropy (IE), spatial frequency (SF), and mutual information (MI) of four objective evaluation indicators. Compared with the average of the five comparison methods, the AG, IE, SF, and MI are increased by 8.19%, 5.34%, 8.54%, and 68.18%, respectively. Thus, the proposed method is reliable and effective.