摘要
在红外弱小目标检测中,由于杂波云层和结构化地面等复杂背景的干扰,导致检测的虚警较多,因此复杂背景的抑制直接影响到目标检测方法的性能。针对这个问题,提出了一种基于Gabor核非局部均值的弱小目标背景抑制方法,并引入了局部结构张量来提高方法抑制背景细节、增强目标信息的能力,此外该操作还减少了方法的运算量,提高了方法的实时性。与小波滤波和高斯核去局部均值方法相比,几组实验结果显示,该方法从主观视觉和数值指标上可较好地抑制复杂背景。
Background suppression is one of the most important subjects in the field of low signal-tonoise ratio and low contrast dim and small targets detection from a sequence of infrared images. Due to the cluttered clouds and structured background, it is difficult to discern the dim and small targets in infrared dim and small targets detection. To improve the detection performance of dim and small targets from complex background, the background suppression method based on Gabor kernel non-local means was presented, and local structure tensor was introduced to suppress background details and enhance target information. The local structure tensor could reduce computational complexity and enhance real-time performance of the method. Compared with wavelet filtering and Gaussian-kernel non-local methods, the proposed method can suppress complex background and enhance the dim and small target effectively according to the visual effect and quantitative index obtained from several groups of experiments.
出处
《红外与激光工程》
EI
CSCD
北大核心
2009年第4期737-741,共5页
Infrared and Laser Engineering
基金
教育部科学技术研究重点项目(108114)
关键词
目标检测
背景抑制
非局部均值
局部结构张量
Target detection
Background suppression
Non-local means
Local structure tensor
