目的 复杂背景中的红外小目标检测易受背景杂波与噪声的干扰，直接利用现有的低秩约束与稀疏表示联合模型存在准确率低、虚警率高及检测速度慢等不足。为了解决这些问题，提出一种基于多尺度红外超像素图像模型的小目标检测方法。方法 首先，采用超像素方法分割原始红外图像，得到无重叠区域的超像素图像，充分利用红外图像的局部空间相关性；然后，引入多尺度理论，融合多个不同尺度下检测的目标图像，增强该方法检测不同尺寸目标的稳健性。结果 针对多幅不同场景下的红外小目标图像进行了实验验证，并选取信杂比增益、背景抑制因子及检测时间作为定量评价指标，以此衡量背景抑制效果及算法运行速度。大量实验结果表明，与Top-Hat、Max-Median、二维最小均方、局部显著性图、红外块图像、加权红外块图像等方法相比，本文方法能有效地去除各种干扰，在背景抑制方面具有更好的效果，且所得背景抑制因子为其他方法的数十倍；与同类方法相比，红外超像素图像模型减少了至少78.2%的检测时间。结论 本文将超像素图像分割与多尺度理论引入低秩约束与稀疏表示联合模型，能够取得更好的背景抑制效果，并且可以适应不同大小目标的检测，实现复杂背景中红外小目标的准确检测。

Objective Infrared small-target detection is a key technology in precision guidance. It is crucial in aircraft infrared search and tracking systems, infrared imaging and guidance systems, and early warning systems for military installations. However, infrared small-target detection in complex backgrounds still encounters challenges. First, due to the long imaging distance, the target is usually very dim and small and lacks a concrete structure and texture information. Second, when strong background clutter and noise exist, such targets are often buried in the background with a low signal-to-clutter ratio. Hence, the issue remains difficult and challenging. Meanwhile, utilizing the existing low rank constraint and sparse representation joint model directly has disadvantages of low accuracy, high false alarm rate, and slow detection. To solve these problems, a small-target detection method based on the multiscale infrared superpixel-image model is proposed. Method The method of constructing an infrared-patch image in prior literature involves setting the sliding window to slide from up to down and left to right of the image at a certain step size. The gray value of each pixel in the sliding window is rearranged into a column vector when it slides to each position. The matrix composed of these column vectors is called the patch image. However, in the process of constructing such a patch image in this manner, the proportion of overlapping area between sliding windows is large, resulting in a high degree of information redundancy. In addition, the constructed patch image has high dimensionality, which leads to a large amount of calculation. To overcome these deficiencies, the superpixel method is adopted to segment the original infrared image and obtain superpixel images with no overlapping area. The method makes full use of the local spatial correlation of the infrared image and avoids the computational burden caused by redundant information. Moreover, introducing multiscale theory then merging the target images detected at different scales can further improve the robustness of the algorithm for detecting targets of different sizes. Result First, experiments are conducted on many infrared small target images with varying situations and levels of noise. Experimental results demonstrate that from the perspective of subjective visual evaluation, the proposed method is robust to different scenes and noise. Experiments are also conducted to verify two aspects, namely, background suppression effect and detection speed. The signal-to-clutter ratio gain and background suppression factor are selected as quantitative evaluation indicators for the background suppression effect. Experimental results reveal that compared with the Top-Hat, Max-Median, two-dimensional least mean square, local saliency map, infrared patch-image, and weighted infrared patch-image methods, the proposed method can effectively eliminate various interferences, exerts a superior effect on background suppression, and can accurately detect infrared small targets in complex backgrounds simultaneously. The background suppression factor of the proposed method is several tens of times that of other methods, and the infrared superpixel-image model reduces the detection time by at least 78.2% compared with similar methods. Conclusion In this study, superpixel image segmentation and multiscale theory are introduced into the low rank constraint and sparse representation joint model. The model can achieve an advantageous background suppression effect and good adaptability to the target size when applied to infrared small-target detection in complex backgrounds. In addition, the proposed method of infrared small-target detection at different scales can be further transformed into parallel processing, which is beneficial for accelerating the detection process of the method. Our future work will focus on reducing the algorithm's complexity and designing a more flexible method for constructing an infrared-patch image.