采用尺度空间理论的红外弱小目标检测方法

为了检测红外场景中尺寸大小变化的弱小目标，针对传统滤波方法中固定大小滤波核对此类特性目标检测表现出的不足，提出一种基于尺度空间理论的红外弱小目标检测方法。首先对弱小目标特性进行分析，提出采用点扩散函数形式的目标模型来描述弱小目标；采用固定自适应邻域的方法对原始红外图像进行预处理，抑制背景杂波，增强目标能量；依据尺度规范化后的拉普拉斯尺度空间对图像不同元素滤波响应的不同，获取图像中的可疑目标，利用可疑目标点与其周围像素的梯度关系得到可疑目标点的中心坐标，并据此得到其在图中的尺寸大小；对每个可疑目标划分一个自适应大小窗口，获取分割阈值，分割出真实目标。实验结果表明，该方法能较好地检测出弱小目标，且具有较低的虚警率。

Infrared dim and small target detection method using scale-space theory

In order to detect small targets with changing size in infrared scene, aiming at the problems in traditional filtering method with fixed size filter, a method for small and dim infrared targets detection based on scale-space theory was proposed. First, the target characteristic was analyzed and point spread function form was used to represent the target model. Then, in order to suppress background clutter and enhance the power of target, fixed adaptive neighborhood method was used in image preprocessing, on the basis of Laplace scale-space after scale standardization which has different filtering responses for different elements, the suspicious targets were obtained in the images, then with the gradient relationship between suspicious target point and its surrounding pixels, the coordinates of the suspicious targets centers and its size were got; Finally, each suspicious targets gained an adaptive window to obtain segmentation threshold and true targets. Experiments results show that, compared with traditional methods, the new method proposed in this paper has a better performance to detect small targets, and has a lower false alarm rate.