为了提高红外图像弱小目标在复杂背景干扰下的检测精度，本文提出了基于多特征相似度差异决策与改进的Top-Hat 变换的红外弱小目标检测算法。该算法通过经典的Top-Hat 的单一结构元素进行分割，形成多尺度结构元素，并依据弱小目标与其周围背景之间的灰度差异，定义了灰度变化映射，通过计算其均值与方差，构建目标决策因子，并将其与多尺度结构元素嵌入到Top-Hat 变换中，形成了新的Top-Hat 变换；随后，联合灰度强度、对比度以及结构信息，建立多特征紧密度差异模型，提取包含真实弱小目标与可疑目标的候选区域；最后，基于弱小目标运动的连续性，引入管道滤波模式，将候选区域中的可疑目标剔除，保留真实弱小目标。实验数据表明：与当前红外弱小目标检测算法相比，在复杂背景干扰下，所提算法的检测精度更高，能够将弱小目标完整地检测出来，具有更好的ROC 特性曲线。

In order to improve the detection accuracy of dim target in infrared image with complex background, an infrared small target detection algorithm based on multi-feature tightness difference decision coupling improved Top-Hat transform. Firstly, multi-scale structure elements was obtained by segmenting the single structural element, and the gray change map was defined according to the gray difference between the small target and its surrounding background, and the target decision factor was constructed by calculating the mean and variance of this map, so the new Top-Hat transformation was formed by embedding it and multi-scale structure elements into classical Top-Hat transform. Then the multi-feature tightness difference model was established to extract candidate regions that contain the real weak and small targets were extracted by combining the gray intensity, contrast and structure information. Finally, the pipeline filtering pattern was introduced to eliminate the suspicious objects in the candidate region and keep the real dim target. The experimental data show that this algorithm had higher detection precision to completely check out the dim target with better ROC curve under the complex background.mapping; pipeline filtering mode