Aiming at the characteristics of a low signal-to-clutter ratio and low false alarm rate in infrared small target detection under different background conditions and focusing on the characteristics of small target energy approaching Gaussian distribution, this paper proposes an infrared small target detection method using improved image local entropy weighted multi-scale based on the image block contrast. First, the mean values of infrared image center blocks and neighborhood blocks were calculated. Thereafter, the mean difference between the center block and the neighborhood block was calculated to highlight small targets and suppress background noise. At the same time, the improved local image entropy of each pixel was calculated to highlight small targets, suppress pseudo targets whose shape is similar to the size of small targets, and suppress corners of large interfering objects. Afterward, the improved image entropy was used to weight the difference between the mean value of the center block and the neighborhood block to obtain a saliency image with a high signal-to-clutter ratio and low false alarm rate. Finally, the adaptive threshold segmentation algorithm was used to obtain the position of the target. The experimental results show that the proposed method is more applicable to a wider range of scenarios than the similar detection methods based on human visual system (HVS), especially in complex backgrounds, and can achieve a lower false alarm rate and higher signal-to-clutter ratio.