针对红外弱小目标检测问题，提出了一种基于图像复杂度的自适应门限目标检测方法.讨论了天空中四类不同区域的图像信息熵.图像信息熵虽然较好地表达了图像的平均信息量，但对图像的突变点不敏感.将它改进得到图像方差加权信息熵，其较好地反映了图像的复杂度特征.将图像方差加权信息熵作为图像复杂度的定量描述，用两种特定的分析模板对图像复杂度进行分析.在目标区域中，两种分析模板得到的复杂度差异较大，而非目标区域的两种复杂度则基本没有差异.算法获取两种分析模板下的复杂度图像，再对两种复杂度图像做差，得到复杂度差值图像.对差值图像建立指数模型得到自适应分割门限完成目标检测.实验结果表明，该方法对低信杂比的红外云层背景弱小目标图像具有良好的检测效果.

In order to detect dim and small infrared targets,a new approach based on image complex degree is proposed.Four image information entropies of different regions are discussed.Image information entropy describes the average information contents efficiently,but insensitive to point mutations.So information entropy weighted by image variance is introduced to describe image complexity.Two specific analysis models are utilized to obtain image complexity features.It is found that there is much difference in target regions,while almost no difference in non-target regions.After the establishment of the self-adaptive exponent models for the difference image of two complexity features,the dim and small targets can finally be detected with self-adaptive threshold processing.Experimental results show that the proposed method can detect dim and small targets in clouds cluster image with low SCR very efficiently.