提出了一种基于多尺度隐式马尔可夫模型的红外图像背景抑制方法来解决红外弱小目标检测技术中复杂背景干扰的问题。根据红外目标和背景杂波所具有的不同统计分布特性, 利用剪切波变换分解后各尺度、各方向子带内和子带间的系数之间的关系, 建立了基于剪切波变换的多尺度隐式马尔可夫模型。通过期望最大化算法计算最优背景参数, 分离红外图像中弱小目标信号和复杂背景杂波, 达到抑制背景、保存并增强目标信号的目的。对真实的和模拟的包含弱小目标的红外图像序列进行了实验验证, 实验结果表明, 与最大中值滤波和局部去均值两种方法相比较, 本文方法对红外弱小目标(SCR≥1.6)的复杂背景从主观视觉和数值指标上都具有良好抑制效果。

A background suppression method based on multi-scale Hidden Markov Mode (HMT) was proposed to remove the complex background clutter in the detection of dim and small targets. According to difference of distributed characteristics between target and background clutter in infrared image, the shearlet transform based multi-scale HMT was estimated by using the different scale and direction relation of inter-scale and cross-subband coefficients of decomposed images. Finally, the expectation-maximization(EM) algorithm was used to calculate the background, separate dim and small targets and background clutter of infrared image and to implement the suppression of background, and the preservation and enhancement of target signals. Compared with Max Median (MMed) and Local Means Remove (LMR) filter from subject inspection and value index, several groups of experimental results demonstrate that the proposed method can suppress the complicated background in dim and small target images effectively(SCR>=1.6).