Adaptive algorithm for small target detection in infrared searching system
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摘要: 为解决红外搜索系统中场景起伏造成的背景预测不准确这一问题，提出了一种自适应调整的空间滤波方法。该算法在估计背景的同时，对背景残差进行计算，根据残差值调整滤波参数，使背景残差趋于最小，以适应背景的起伏。当背景包含较多复杂因素时，不利于目标提取，多尺度形态学算子通过不同尺度不同形态的结构体参与计算，可以全面地估计背景，进一步抑制背景残差，再通过计算图像全局阈值，自适应分割出潜在目标。采用并行运算，可将算法实现于现场可编程器件（FPGA）上。试验结果表明：即使当场景较复杂，场景信噪比较低时，依然可以使处理后的图像信噪比大于3，从而可显著提高红外搜索系统的检测概率，实现弱小目标的检测。Abstract: Aiming at the small object detection in infrared searching system, a novel algorithm of adaptive spatial filtering is proposed on the basis of the model analysis of infrared images. The background residual errors are calculated in the algorithm while the background is estimated, and the filtering parameters are adjusted to adapt to the fluctuation of the background according to the values of the residual errors. The multi-scale morphology operator is used in the algorithm. This method is suitable to the infrared small object detection in clutter background, and can reserve the information of objects totally. The method is run on the platform of FPGA, and used in an infrared searching system. The experiment results presented in this paper show that the signal-to-noise ratio (SNR) of the processed images can reach to 3 even if the scene is complex and SNR is low. It has a good result on real-time processing and is easy to be realized in engineering.
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