针对复杂背景下的红外弱小目标检测问题，提出了一种基于形态学带通滤波和尺度空间理论的红外弱小目标检测算法。采用形态学带通滤波对红外图像进行预处理，得到红外弱小目标的潜在区域；利用高斯差分算子获得预处理后的红外图像的尺度空间，并通过尺度空间的极大值检测获得候选目标的位置和尺度；通过对候选目标的信杂比进行阈值化实现红外弱小目标的检测。实验结果和现有方法的对比证明了算法的有效性和稳健性。

A novel approach is proposed to detect infrared dim target from cluttered background by using morphological band-pass filtering and scale space theory. The infrared image is pre-processed by means of morphological band-pass filter, which results in regions of interest (RoI) containing dim small targets. Then, difference-of-Gaussian function is adopted to obtain scale space of pre-processed infrared image. Scale space maximum detection is then performed to generate candidate targets with their positions and scales. Infrared dim small target detection is achieved by using thresholding signal-to-clutter ratio of candidate targets. Experimental results on real-world infrared images and comparisons with state-of-the-art methods can demonstrate the effectiveness and robustness of the proposed approach.