基于加权块对比度的红外小目标检测方法

Infrared Small Target Detection Method Based on Weighted Patch Contrast

针对不同背景条件下,红外弱小目标检测信杂比低、虚警率高的特点,重点利用小目标能量接近高斯分布特性,提出一种利用改进的图像局部熵加权多尺度的基于图像块对比度的红外小目标检测方法。首先,计算红外图像中心块和邻域块的均值;然后,计算出中心块和邻域块的均值差异达到凸显小目标、抑制背景噪声的效果,同时计算各个像素点的改进局部图像熵以凸显小目标、抑制形状与小目标大小近似的伪目标以及大面积的干扰物体的角点;之后,利用改进的图像熵加权中心块和邻域块的均值差异值,得到高信杂比、低虚警率的显著度图像;最后,利用自适应阈值分割算法获取目标的位置。实验结果表明,与同类基于human visual system(HVS)检测方法相比,所提方法适用场景更广,特别是在复杂背景下,能达到更低的虚警率、更高的信杂比。

Aiming at the characteristics of a low signal-to-clutter ratio and low false alarm rate in infrared small target detection under different background conditions and focusing on the characteristics of small target energy approaching Gaussian distribution,this paper proposes an infrared small target detection method using improved image local entropy weighted multi-scale based on the image block contrast.First,the mean values of infrared image center blocks and neighborhood blocks were calculated.Thereafter,the mean difference between the center block and the neighborhood block was calculated to highlight small targets and suppress background noise.At the same time,the improved local image entropy of each pixel was calculated to highlight small targets,suppress pseudo targets whose shape is similar to the size of small targets,and suppress corners of large interfering objects.Afterward,the improved image entropy was used to weight the difference between the mean value of the center block and the neighborhood block to obtain a saliency image with a high signal-to-clutter ratio and low false alarm rate.Finally,the adaptive threshold segmentation algorithm was used to obtain the position of the target.The experimental results show that the proposed method is more applicable to a wider range of scenarios than the similar detection methods based on human visual system(HVS),especially in complex backgrounds,and can achieve a lower false alarm rate and higher signal-to-clutter ratio.