基于改进复杂度的红外弱小目标区域检测算法

方差加权信息熵作为稳健的红外背景复杂程度定量描述指标, 在红外弱小目标检测中取得了不错的效果, 但由于其计算复杂, 导致算法实时性差, 很难在工程上应用。为了能快速地在红外复杂天空背景中识别到弱小目标区域, 对传统的基于图像方差加权信息熵的滤波算法进行改进。先对图像进行显著性区域分割, 粗略地得到显著性区域, 然后对显著性区域计算双分析模板区域方差加权信息熵差值, 根据复杂天空中典型区域的双分析模板区域方差加权信息熵差值的特点将候选目标区域识别出来。实验表明, 用本文算法既可以排除大量的复杂天空背景干扰区域, 又大幅缩短了算法运行的时间。

Abstract

Information entropy weighted by image variance is a robust quantitative indicator describing the complexity of image. It can achieve good results by using information entropy weighted by image variance to detect the infrared small target. However, it is difficult to apply in engineering application due to its complex calculation and poor real-time performance. To recognize the small target regions under infrared complex sky background quickly, we improve the traditional image filtering algorithm, which uses information entropy weighted by image variance. Images are segmented according to their saliency first. Significant regions are selected roughly, and only the information entropy weighted by image variance of the dual-mode regions of the salient regions is calculated. Then, the candidate target regions are recognized according to the typical regional features of the information entropy weighted by image variance of the dual-mode regions under complex sky background. The experimental results show that the proposed algorithm can eliminate the disturbance of the complex sky background, and reduce the running time of the algorithm.

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朱婧文, 刘文好, 印剑飞, 刘礼城. 基于改进复杂度的红外弱小目标区域检测算法[J]. 激光与光电子学进展, 2018, 55(10): 101006. Zhu Jingwen, Liu Wenhao, Yin Jianfei, Liu Licheng. Infrared Small Target Regions Detection Based on Improved Image Complexity[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101006.

基于改进复杂度的红外弱小目标区域检测算法

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