目的 针对Robinson guard滤波器的局限性和红外图像背景抑制问题，提出一种新的红外背景抑制滤波算法。方法 首先通过形态学Tophat算子对图像背景进行抑制，然后对背景抑制后的图像采用改进的Robinson guard滤波器进一步凸显目标，并通过阈值化分割出感兴趣区域，在此基础上，利用Unger平滑去除小的噪声点，最后用局部信杂比（SCR）和移动式管道滤波剔除伪目标，实现运动小目标的准确定位。结果 采用3组不同的红外背景图像序列进行实验，所提算法对不同背景均有很好的抑制效果，与传统Robinson guard滤波方法相比，本文算法不仅能更有效地保留目标的特征信息，而且对3组图像序列的小目标的检测率分别提高了1.1%、2%、11%，虚警率分别降低了14%、12%、16%。结论 本文算法能有效地检测出小目标，具备较高的准确性，对于低信噪比的图像具有良好的适应性。同时，本文算法具有较高的实时处理能力，有利于实现实时性技术应用。

Objective Infrared small target detection is an important branch of computer vision applications. It has extensive applications in the military and civil fields, such as in precision guidance, security monitoring, and medical imaging systems. Infrared image preprocessing includes noise reduction and background suppression, and the main factor that affects small target detection is background clutter interference. Therefore, infrared image preprocessing prioritizes background suppression algorithms. Among numerous background suppression algorithms, the Robinson guard filter is a widely used filter based on a guard band. This filter cannot only achieve excellent suppression effect, but can also integrally retain the edge and internal information of small targets. However, the filtering mechanism of the Robinson guard filter is more sensitive to noise and cannot effectively deal with small targets with varying sizes. Given the limitation of the Robinson guard filter, this study proposes a small target detection method based on infrared background suppression. Method First, the background of infrared images is suppressed via Tophat transformation of the mathematical morphology to decrease the influence of noise on followup processing and reduce the sensitivity of the filtering mechanism of the Robinson guard filter to noise. Second, the modified Robinson guard filter is adopted to further suppress the background and to enhance the small targets of the processed images. Then, the regions of interest are identified using an adaptive threshold method. On this basis of this method, Unger smoothing is performed to remove small noise points. Then, we apply the local signal-clutter ratio (SCR) to eliminate residual background clutter after considering the difference between the gray value of the small targets and the gray value of the background, Finally, a moving pipeline filter is applied based on the motion continuity of the infrared small targets to exclude false positives, and the locations of the true small targets are accurately confirmed. Result We use three groups of image sequences with different backgrounds as experimental objects. Furthermore, we provide filtering performance (e.g., GSNRG and BSF) and detection performance (e.g., detection rate and false alarm rate) as evaluation indices. Test results show that the proposed method outperforms the Tophat algorithm, the max-mean algorithm, the Robinson guard filter, and the improved local contrast measure (ILCM) algorithm in terms of background suppression and detection performance. Compared with the traditional Robinson guard filter, the proposed method cannot only retain the characteristics of the small targets significantly, but can also improve the detection rates of the three groups of image sequences by 1.1%, 2%, and 11%, respectively. Moreover, it can decrease their false alarm rates by 14%, 12%, and 16%, respectively. The proposed method can achieve superior detection performance and can fully satisfy real-time requirements based on the test results. Conclusion The experiment results demonstrate that the proposed method exhibits better performance in terms of background suppression and small target detection than the other four algorithms. The method also exhibits good adaptability in low SCR images. Meanwhile, it possesses high capability for real-time processing, which is conducive to realizing technological applications.