为了有效地检测复杂背景下的红外弱小目标，提出了一种基于横纵多尺度灰度差（HV-MSGD）的方法来增强弱目标，并通过距离和像素差异来实现对背景强边的抑制。目标区域与周围区域之间存在不连续性，为了加强它们的差异，HV-MSGD与双边滤波（BF）相结合，可以在抑制背景的同时提高目标强度。进一步通过自适应局部阈值分割和全局阈值分割来提取候选目标。为了进一步验证对单帧检测的影响，将上述单帧检测算法与改进的无迹卡尔曼粒子滤波器（UPF）相结合，实现轨迹检测。实验结果表明，该方法在弱信噪比（SNR）下优于其他方法，在抑制背景的同时可以增强目标，增强效果是其他方法的6-30倍。在实验中，输入信噪比分别为2.78,1.77,1.79,1.13和1.16。图像处理后，背景抑制因子（BSFs）分别为13.48,21.33,11.73,20.63和121.92，信噪比增益（GSNRs）分别为40.09,71.37,27.53,12.65和131。该方法的检测概率（Pd）也优于其他算法。当误报率（FARs）为5×10-4, 1×10-3, 1×10-3, 1×10-5和7×10-6，计算五组真实序列图像的Pd为94.4％，92.2％，91.3％，95.6％和96.7％。

In order to effectively detect weak and small infrared targets under complex background, a single-frame method based on horizontal-vertical multi-scale grayscale difference （HV-MSGD） is proposed to enhance weak targets, and the strong edges of background are suppressed by the difference between the distance and grayscale values. There is discontinuity between the target area and the surrounding area. To strengthen their differences, HV-MSGD combined with bilateral filtering （BF） can increase the intensity of the target while suppressing the background. Candidate targets are further extracted by adaptive local threshold segmentation and global threshold segmentation. In order to further verify the impact on single-frame detection, the above-mentioned single-frame detection algorithm is combined with an improved untraced Kalman particle filter （UPF） to implement trajectory detection. The experimental results show that this method is better than other methods under weak signal-to-noise ratio （SNR）. It can enhance the target while suppressing the background, and the enhancement effect is 6-30 times that of other methods. In the experiments, the input signal-to-noise ratios were 2.78, 1.77, 1.79, 1.13, and 1.16, respectively. After image processing, the background suppression factors （BSFs） are 13.48, 21.33, 11.73, 20.63, and 121.92, and the signal-to-noise ratio gains （GSNRs） are 40.09, 71.37, 27.53, 12.65, and 131, respectively. The probability of detection （Pd） of this method is also superior to other algorithms. When the false alarm rates （FARs） are 5×10-4, 1×10-3, 1×10-3, 1×10-5, and 7×10-6, the Pd values of the five sets using real sequence images are calculated to be 94.4%, 92.2%, 91.3%, 95.6% and 96.7% respectively.