基于曲面波变换的红外弱小目标背景抑制

Infrared small and weak targets background suppression based on surfacelet transform

提出了一种基于曲面波变换的弱小目标背景抑制新方法,解决红外搜索跟踪系统探测远距离弱小目标中复杂结构化背景抑制难题。根据红外图像中目标和背景杂波的特性,首先,采用曲面波变换对序列图像进行多尺度、多方向和各项异性分解,提取图像的多尺度和方向细节特征;其次,根据目标和背景杂波信号的差异,通过应用设计的核函数调整分解后的各尺度和方向的子带系数值;然后,重构修改后的各子带,从而将红外图像中弱小目标和背景杂波分离,达到抑制背景的目的;最后,采用自适应阈值分割技术得到真实目标点,最终实现对弱小目标的精确探测。实验结果显示,与局部去均值和最大中值滤波方法相比较,该方法能有效地检测出信杂比(signal-to-clutter ratio,SCR)在1.6以上的目标。

For infrared images with the characteristics of low signal-to-clutter ratio （SCR） and contrast ratio （CR）, a small and weak target background suppression method based on surfacelet transform is proposed to solve the problem, and a designed function is introduced to boost the ability to suppress false information by background structure. Firstly, the surfacelet transform is adopted to decompose the input infrared image sequences, which extracts multi-scale, anisotropic and directional detail features of the image. Then, according to difference between target and background clutter signal, a kernel function is introduced to suppress background details and enhance target information for suppression background. Finally, the target image is obtained by using an adaptive thresholding method. Several groups of experimental results demonstrate that the proposed method can segment the infrared target image effectively compared with several classical infrared small and weak target detection methods （SCR〉1. 6）, such as local remove means （LMR） and max median （MMed） methods.