In the infrared image of complex background,weak and small targets are usually submerged in the highlight edge and strong clutter.In this paper,a method for detecting small infrared targets based on improved weighted local contrast is proposed.Using the local characteristics of the small target,a weighting function is established to multiply the difference point between the target and its background neighborhood to highlight the target,which in turn is compared with the adjacent background neighborhood to suppress the effect of complex background.Through the isotropy of the target and the anisotropy of the background,the background suppression model is created by the six direction gradient decision method to further suppress the highlighted edges,so as to reduce the false alarm rate and improve the detection rate.Finally,the two are combined by convolution calculation,and the real target is detected by adaptive threshold segmentation.The experimental results show that the proposed algorithm has strong robustness under complex background and strong clutter interference.