根据弱小目标的成像特性, 提出了基于邻域局部最大均值与多尺度形态学滤波的目标检测算法。通过滑动窗口判断图像中心是否为最大值, 若是, 则用中心点四邻域的两个方向的最大均值替代中心点；否则, 计算其四邻域方向极大二值的梯度, 根据加权系数计算赋给中心点。遍历整幅图像, 用来消除噪声和改善初始图像的信噪比。然后, 再对图像进行多尺度的形态学滤波, 可以有效地估计背景并将背景从原始图像中移出。改进的自适应分割方法计算阈值之后, 从候选点中来提取目标。对序列图像采取多帧关联处理, 可以进一步降低虚警率。实验结果表明, 该算法易于实现, 能提高检出概率, 较好并完整地检测出目标, 且降低虚警率。

According to the imaging features of dim targets, we put forward a target detection algorithm based on the local maximum mean of neighborhood and multi-scale morphological filtering.It is determined whether the image center is the maximum or not by the sliding window.If it is the maximum, the center pixel will be replaced by the maximum mean of the two directions of its four neighborhoods.Otherwise, the center will be assigned according to the calculated weighting coefficient by figuring out the two maximum gradients of four neighborhoods.It is implemented through the whole image for eliminating the noise and improving the signal-to-noise ratio of the original image.Then, multi-scale morphological filtering of the image is carried out and the background can be estimated effectively and be removed from the original image.After the threshold is calculated by using the improved adaptive segmentation method, the targets are extracted from candidate points.Multi-frame association is applied to sequential images for further reducing the false alarm rate.Experiments show that, the method is easy to implement, and can detect the whole target with high detection probability and low false alarm rate.