基于多方向梯度和形态学算法的红外弱小目标检测

受多种因素的影响,通常获得的红外图像信噪比低、对比度差,为目标的提取带来一定的困难。在分析弱小目标方向梯度的基础上,结合形态学算法,提出一种新的红外弱小目标检测算法。首先在待检测点四邻域方向上选取4个参考点,根据该方向待检测点与参考点之间的多级梯度特征,确定出潜在目标;然后利用结构元可调节的特性,选择合适的结构元素,通过形态学处理,剔除噪声点并最终确定出目标。实验表明,该算法计算简单,无需预测背景,可在低信噪比图像中有效检测弱小目标。

Small Infrared Target Detection Based on Multi-direction Gradient and Morphology Algorithm

Influenced by many factors, the obtained Infrared images usually have a low signal-to-noise ratio （SNR） and contrast, which brings a certain difficulty to target extraction. Based on analyzing the direction gradient of the target, with morphology algorithm, a new detection algorithm of small Infrared targets is introduced. Firstly, four reference points at the direction of four neighbor domain of the tested point are chosen. The potential targets are determined according to multi-level gradient feature between the tested point and the reference point at the direction. And then, the adjustable feature of structure element is used to choose appropriate structure element. Finally, through morphology processing, noise points are eliminated and the target is determined. The algorithm has simple calculation and in no need of predicting background. Experimental results show that small targets can be detected effectively in low signal-to-noise ratio （SNR） images.