中文摘要：

针对红外目标检测中经常遇到目标比背景小很多造成目标分割失败的问题,以及常用阈值选取方法仅依赖于图像直方图的概率信息而未直接考虑类内灰度分布的均匀性,提出一种基于目标与背景面积差和修正灰度熵的阈值分割算法.算法首先采用自适应中值滤波和均值滤波进行图像预处理,以减除噪声干扰.然后给出了修正灰度熵公式,该公式能很好地解决熵计算中出现的无定义问题,并利用目标与背景面积差较大的特点,构造得到最终的阈值选取公式.最后,在直方图上采用优化搜索策略,进一步降低算法的计算复杂度.实验结果表明,与Otsu法、最大熵法相比,该算法抗噪性能好,能有效实现红外小目标的分割,且运算时间至少减少了80％左右.

英文摘要：

Incorrect segmentation always occurs because the target is much smaller than its background. Common threshold selection methods only rely on the probability information from the image histogram without directly considering the uniformity of inter-class gray distribution. Aiming at these problems a threshold selection algorithm based on the area difference between target and background and modified gray entropy is proposed. Firstly, an adaptive median filter and a mean filter are used for image preprocessing so as to reduce the noise. Then, a formula of a modified gray entropy is presented ,which can well solve the problem of undefined value in the entropy calculation. The final formula of threshold selection is constructed which exploits the characteristics of large area difference between target and its background. Finally, in order to further reduce the computation complexity of algorithm, an optimal search strategy of threshold in histogram is put forward. The experimental results show that the presented algorithm can effectively remove image noise and segment infrared small targets. Compared with Otsu algorithm and maximum entropy algorithm, and the operation time is reduced by about 80%.