针对红外图像中由复杂背景和目标多形态带来的单帧检测暗弱小目标比较困难的问题,提出了一种先进行阈值分割粗提取,后进行多点信噪比精检测的算法。在粗提取阶段,提出了改进的基于稳健主成分分析(RPCA)的阈值分割算法,利用邻域稀疏度均值与整幅稀疏图像均值的比值进行阈值分割,从而进一步剔除孤立噪点和背景云层边缘的杂波。在精检测阶段,提出了基于统计特性的多点恒虚警检测算法,统计候选点在邻域内每个像元的信噪比,利用虚警率门限和统计数量阈值筛选目标点,从而克服由小目标能量弥散带来的多形态特征问题。实验结果表明,所提算法在复杂背景下的探测率达到95.6%,与利用单像元和邻域像元均值计算信噪比的方法相比,虚警率分别降低了56.1%和47.1%。

To address the difficulty in detecting a dim-small target in single frame image caused by the complex background and polymorphism of the target, a method of rough extraction for threshold segmentation and precise detection for multi-point signal-to-noise ratio (SNR) is proposed. In the rough extraction stage, an improved threshold segmentation algorithm based on robust principal component analysis (RPCA) is proposed. The ratio of the mean value of the neighborhood sparseness to the mean value of the whole sparse image is used for the threshold segmentation, so as to further eliminate the isolated noise and the edge clutter of background cloud. In the precise detection stage, a multi-point constant false alarm detection algorithm based on statistical characteristics is proposed. The SNR of each pixel of candidate points in the neighborhood is obtained, and then the target point is extracted based on the false alarm rate threshold and statistical quantity threshold. The problem of polymorphic features caused by the dispersion of target energy will be overcome. Experimental results show that the detection probability of this algorithm reaches 95.6% under complex background, and the false alarm rate is 56.1% and 47.1% lower than that of single pixel and neighboring pixel based SNR computing methods, respectively.