由于传统的背景抑制方法没有充分利用信号的方向信息, 使其不能有效区分背景边缘和红外弱小目标, 从而造成背景抑制结果中有较多的背景泄露.针对上述问题, 本文利用改进的剪切波变换和引导滤波, 提出了一种新的背景抑制方法.首先,采用改进的剪切波变换对红外弱小目标图像进行多尺度和多方向分解, 将图像分解得到不同的高频子带系数和低频子带系数;其次, 利用目标信号与边缘在方向上的差异, 采用自适应引导滤波对高频子带系数进行处理;再次, 对分解后的低频子带系数和处理后的高频子带系数进行改进的剪切波逆变换, 得到预测的背景图像;最后, 将原图像与背景预测图像相减获得背景抑制且目标增强的红外图像.为了验证本文方法的有效性, 采用多组实验对其进行验证, 并与经典的Max-Median、TDLMS和Top-hat等方法作比较.多组实验结果均表明本文方法在主观视觉和客观评价指标方面均优于其它三种经典方法, 可有效提高红外搜索跟踪系统对红外弱小目标的探测概率.

Because traditional background suppression methods don't take full advantage of direction information, small target and edges can't be distinguished effectively and some background may expose. A new method based on improved shearlet transform and image guide filter was proposed. Firstly, nonsubsampled wavelet transform and shear directional filter bank is adopted to decompose the infrared image into multi-scale and multi-direction to obtain high frequency and low frequency coefficients. Then the image guide filter is used to adjust the high frequency coefficients of each sub-bands to suppress the dim and small target signal and predict the background. Then the estimated background is obtained by the inverse transformation. At last, the suppression result is obtained by subtracting the estimated bcakground from the original image. Several group of experimental results demonstrate that the presented method can suppress the background and enhance the small target signal effectively, compared with several classical methods, such as Max-Median、TDLMS and Top-hat. The proposed method can effectively improve the detection probability of infrared search and tracking system for infrared dim and small target.