A saliency detection algorithm for infrared images based on texture coarseness was proposed to detect the saliency of targets owing to a low image contrast. Firstly, Tamura's principle of calculating coarseness was researched, and a new method to calculate the coarseness was presented by analysis and evaluation of the coarseness. Then the image was decomposed into a set of super pixels and the maximum mean intensity difference of the super pixels was calculated. Furthermore, the best scale of super pixels was defined by using maximum mean intensity difference to be a measure of the texture coarseness. Finally, the region of every super pixel was expanded isotropically and the saliency of infrared image was measured based on the local contrast and grey information of the texture coarseness with the weight of intensity. The effectiveness of algorithm was verified. Results show that coarseness based on the proposed method remains unchanged under a noise level of 10% and the homogeneity is better in the feature map of coarseness. Meanwhile, there are many miscellaneous points in Tamura's feature map of coarseness. Compared with other methods of saliency detection for infrared images, the proposed algorithm has the highest hit rate, up to 0.752. The algorithm exploits the feature of texture coarseness, and provides a new selection method for the saliency detection of infrared images.