基于多向背景预测的红外弱小目标检测

杂波背景中的弱小目标检测是红外图像处理中的一个重要问题。普通的二维滤波背景预测方法可以用来检测图像中的小目标，但是也存在对复杂场景的适应性差，杂波边缘虚警高的问题。通过分析二维最小均方滤波背景预测算法的方向特性，在对图像四邻域滤波残差进行像素级加权融合后，得到了一种基于多方向融合自适应滤波背景预测的弱小目标检测方法。对构造图像和实际红外云杂波场景中的小目标检测仿真表明，该方法对不同背景适应性较强，在保持目标检测概率的同时显著抑制了杂波边缘虚警，有效提高了杂波背景中小目标的检测性能。 

Detection of Dim Infrared Targets by Multi-Direction Prediction of Background

The detection of dim targets in clutter background is an important problem in infrared image processing. The traditional two-dimensional least mean square (TDLMS) filter has been used to detect the dim targets in infrared image sequence recently, but there are some problems with these methods such as a bad adaptability with complex scenes, anisotropy, and a high probability of false alarm (PFA) on the edge of clutter. By analyzing the directional feature of TDLMS filter, we fused the four neighborhood filters and then got a detecting algorithm based on four-direction fusion adaptive two-dimensional least mean square filters (FD-TDLMS) at last. Simulations were conducted on the detecting of dim targets in cloud background and artificial images, and they have shown that this algorithm has a better adaptability with complex background, a lower false alarm probability on the edge of clutter than traditional method when has a good detect probability at the same time.