红外复杂背景中的弱小目标检测问题可看作是马尔可夫随机场理论框架下红外图像中背景与目标的二元分类标记问题.基于马尔可夫随机场后验概率模型, 提出利用先验的目标信杂比信息和图像局部统计特性构建观测图像后验概率模型的方法, 并采用经典ICM (Iterated conditional mode)方法对图像最优标记结果进行估计.仿真试验结果表明, 算法在保证目标标记结果准确率的同时, 有效降低了背景的误标记概率; 且由于采用局部统计特性进行建模, 算法有效降低了模型参数与标记结果间的关联性, 提高了最优标记估计的收敛速度.

Dim small target detection problem in infrared complex background was formulated as a binary classification problem of background and target in the theoretical framework of Markov random field (MRF). Based on the posterior probability model of MRF, a method using prior information of target SCR (signal-to-clutter ratio) and local statistic characteristic of infrared image was proposed to construct the posterior probability model of observed image. The classic iterated conditional mode (ICM) was used to estimate the optimal labeling image. Simulation and experimental results show that the proposed algorithm effectively reduces the false labeling probability of background, while maintaining a high probability of correct labeling of target. In addition, for using image’s local statistic characteristic in modeling, the proposed algorithm also reduces the correlation between labeled results and model parameters which contributes to improvement on the convergence speed of estimating the optimal labeling.