摘要
针对红外图像的小目标检测问题,提出了一种基于局部尖峰特性的检测方法.首先分析红外小目标的局部灰度特性,提出了一种红外目标的峰值特性判据;然后依据目标的峰值特性判据和时域特性,设计了一种目标检测的快速算法,算法先基于子块预选出局部极大值点,把后续运算限于各极大值点处以减少运算量,再根据极大点值在各方向上的灰度下降判断其尖峰特性;最后利用帧间的连续性滤去噪音引起的伪目标.实验表明本文的算法具有很快的处理速度,且能有效滤去图像中的随机噪音.
For the problem of small target detection in infrared image,a method based on the local saliency is proposed.The feature on the local gray-scale of small targets in infrared image is analyzed,and a criterion is proposed to check the feature of peak value.Based on the criterion to check peak value and the characteristic of small target on time domain,a fast algorithm is designed.Firstly,local max points are selected and the follow-up computing is limited to these points to reduce the computation.Then peak values are checked based on the decline of gray-scale.Finally,false targets caused by noise are removed based on the continuity between frames.Experiments show that this algorithm has a high processing speed,and can effectively filter out the random noise in the image.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2013年第2期228-233,共6页
Acta Photonica Sinica
基金
国家自然科学基金(No.61172111)资助
关键词
小目标检测
红外图像
局部梯度
帧间连续性
Small target detection
Infrared image
Local gradient
Continuity between frames
