基于局部峰值的红外弱小目标快速检测

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.

Infrared Small Target Fast Detection Based on Local Saliency

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.