针对现有红外弱小目标检测方法背景抑制不充分、计算复杂度高，导致红外侦察预警系统虚警率高、响应速度慢的问题，提出一种基于双邻域差值放大的高动态红外弱小目标检测方法。首先，分析真实红外图像中目标与邻域的均值特性；然后，计算出目标区域与内外双层邻域的差异，从而提升亮、暗弱小目标的局部对比度并抑制复杂背景和噪声；最后，利用自适应阈值分割算法获取目标的位置。针对信杂比增益和背景抑制因子难以客观评价红外图像序列的目标增强和背景抑制性能的问题，提出一种目标轨迹显著图评价方法，有效评价红外图像序列目标检测性能。实验结果表明：与同类检测方法相比，该方法的信杂比增益与背景抑制因子分别提高了12%与10%，运行时间约缩短了34 ms，是一种有效可行的高动态红外弱小目标检测方法。

Aiming at the problems of insufficient background suppression and high computational complexity of existing infrared small target detection methods, which lead to high false alarm rate and slow response speed of infrared reconnaissance and early warning system, a high-dynamic infrared small targets detection method based on double-neighborhood difference amplification was proposed. Firstly, the mean value characteristics of target and neighborhood in real infrared images was analyzed. Then, the difference between the target area and the inner and outer bilayer neighborhood was calculated, so as to improve the local contrast of bright and weak small targets and suppress complex background and noise. Finally, an adaptive threshold segmentation algorithm was used to obtain the target location. Aiming at the problem that signal-to-clutter ratio gain and background suppression factor were difficult to objectively evaluate the performance of target enhancement and background suppression of infrared image sequences, a target trajectory saliency graph evaluation method was proposed to evaluate the target detection performance of infrared image sequences effectively. Experimental results showed that compared with similar detection methods, the signal-to-clutter ratio gain and background suppression factor of this method were increased by 12% and 10%, respectively, and the running time was shortened by about 34 ms. Therefore, this method is an effective and feasible method for high dynamic infrared small targets detection.