针对现有方法在复杂云层和强杂波的干扰环境下的高虚警率或低检测率的问题,提出一种基于视觉特征融合的机载红外弱小目标检测方法。首先,利用Laplace算法对原始图像进行锐化处理,提取图像的轮廓边缘,并将其叠加到原始图像上,目的是增加真实目标与疑似目标的像素强度。然后,根据目标的梯度特征,采用局部多向梯度方法对处理后图像中的复杂背景和强杂波进行抑制。其次,根据图像的灰度差异特征,采用局部灰度差方法适当地增强目标。最后,将通过视觉特征信息获取的图像融合,突出目标的显著性,并对目标进行自适应阈值处理,实现目标的精准检测。实验结果表明,与其他方法相比,所提方法在信杂比、背景抑制因子及检测率指标上得到显著提升,且具有较低的虚警率。

In this paper, a dim target detection method in airborne infrared images based on visual feature fusion is proposed. The proposed method aims at improving the high false alarm rate or low detection rate achieved by existing methods in complex cloud and strong clutter interference environments. Initially, the original image is sharpened using Laplace algorithm to extract the contour edge, which is added to the original image. The purpose is to enhance the pixel intensity of real and suspected targets. Subsequently, based on the gradient characteristics of the targets, the local multidirectional gradient method is used to suppress the complex background and strong clutter in processed images. Next, based on the gray difference characteristics of the images, the local gray difference method is employed to properly enhance the target. Finally, the images acquired by visual feature information are fused to highlight the saliency of the targets, and the adaptive threshold is used to achieve accurate target detection. The experiment results verify that compared with other methods, the proposed method significantly improves the signal-to-clutter ratio, background suppression factor, and detection rate. It also achieves a lower false alarm rate.