为了有效解决动态背景变化导致弱小目标检测率低的问题, 文中提出了改进时空滤波的红外弱小目标检测算法。首先在分析红外图像成像特性的基础上, 针对目标区、背景区和边缘轮廓区不同梯度特性的差异, 提出改进的各向异性空域滤波算法, 该算法充分利用空间域的梯度信息来构建不同方向的扩散滤波函数, 并结合图像不同特性的梯度差异选取扩散函数值最小的两个方向的均值作为时域滤波结果, 以最大限度地保留目标信号; 接着为有效增强弱小目标的能量, 针对高阶累积量仅利用像元点时域信息来构建能量增强的不足, 提出了一种结合时空邻域块的能量增强算法, 实验表明, 本文提出的算法能有效提升动态场景下的弱小目标的检测能力。

To effectively solve the problem of low detection rates of dim and small targets caused by dynamic background changes, a detection method based on spatio-temporal filtering is proposed in this paper. Based on an analysis of the imaging characteristics of infrared images, an improved anisotropic spatial filtering algorithm is proposed to evaluate the difference in various gradient characteristics of the target area, background area, and edge contour area. The algorithm fully utilizes the gradient information in the spatial domain to construct the diffusion filter function in different directions. According to the gradient difference in various characteristics of the image, the mean value of the two directions with the smallest value of the diffusion function is selected as the result of spatial filtering to retain the target signal to the maximum extent. To effectively enhance the energy of dim and small targets and address the shortcomings of high-order cumulants that only use the temporal domain information of pixel points for energy enhancement, an energy enhancement algorithm based on spatial-temporal neighborhood blocks is proposed. Experimental results reveal that the proposed algorithm can effectively enhance the detection of dim and small targets in dynamically changing scenes.