在对Facet模型的方向导数特征进行研究的基础上,针对红外弱小目标提出了一种利用局部方向相对极差计算显著性的方法,以快速有效地提取复杂背景下的红外弱小目标。基于弱小目标单帧检测理论,首先计算原始图像的Facet方向导数特征,然后在Facet方向导数特征图的局部内,沿着导数方向计算相对极差对比度显著图。通过对各个方向上的相对极差对比度显著图进行融合得到最终的显著性图像。最后,采用适当的阈值分割从该图像中提取目标。实验结果表明,本文算法对复杂红外弱小目标图像具有很高的信杂比增益和背景抑制因子。另外,该算法的计算复杂度低且可利用二维卷积加速计算,具有良好的算法实时性,适用于各种处理器平台的工程实现。

Based on the study of directional derivative characteristics of Facet model, a detection method for infrared weak targets is proposed. This detection method uses local relative extreme differential value at the multi-directional lines to calculate the significance of small target, which is very effective for fast extraction of targets under complexbackground. This method is based on the theory of single frame detection of weak target. Firstly, the Facet wizard number characteristics of the original image are calculated. Secondly, in the local part of the Facet wizard number feature graph, the relative extreme difference contrast is calculated along the current direction. Then the relative extreme difference contrast in each direction is fused to obtain the final significant image. Finally, the target is extracted with appropriate threshold segmentation for the final significant image. The experiment result shows that the proposed algorithm has high signal-clutter gain and background inhibitor for complex infrared weak target images. In addition, the computational complexity of the algorithm is low and can be calculated by using two-dimensional convolution acceleration. As a good real-time algorithm, it is beneficial to the engineering implementation of various processor platforms.