起伏背景下红外目标检测算法研究

IR Target Detecting Algorithm Under Background of Fluctuation

为了准确实现起伏背景下的目标分割,提出了一种结合信息熵和区域生长的红外小目标图像检测方法。在分析红外图像特性的基础上,对原始图像进行自适应平滑滤波,使得图像在保持目标强边缘的前提下抑制高斯白噪声;利用目标边缘在其邻域内起伏大的特点,使用基于灰度级-邻域灰度级绝对差G-G直方图的最大熵进行图像分割;利用最大熵求解的阈值选取种子点,将种子点在已分割的图像上进行区域生长。实验结果表明,所提方法在背景起伏较大的情况下具有较好的目标检测性能。

An algorithm combining the information entropy and region growing method is put forward for the accurate segmentation of the IR small target image in background of fluctuation. First,the IR original image is filtered using the adaptive smooth filter after analyzing the image features,thus the Gaussian noise is restrained without destroying the strong edge of the target. Then,the image segmentation is implemented using the maximum entropy algorithm based on the Gray level-Gray absolute difference（ G-G） histogram,by using the feature that the target edge is high in gray level and changes rapidly in its surrounding region. After the image segmentation,the seed points are selected according to the threshold obtained in the maximum entropy algorithm. And the region growing method is realized in the segmented images. Experiment results show the effectiveness of the methods given here in target-detection under background of fluctuation.