多量级多向梯度海空复杂背景红外弱点目标检测

由于红外图像一般带有较大的噪声，采用传统的目标检测方法效果不理想。本文提出了一种新的海空背景下受强杂波和噪声污染的红外图像弱点目标检测算法。用多个量级梯度对图像目标进行检测，并对检测的结果进行了表决融合。结果表明，基于表决融合的多量级多向梯度检测消除了云层、海浪和海天线等背景干扰，在实现高检测概率的同时，不仅可以达到较低的虚警概率，而且可检测信杂比为1的点目标。

Detection Based on Multi-Degree and-Orientation Gradient Fusion for Weak Infrared Dot-Objects

Experiments indicate that the traditional methods of infrared image detection are not ideal because of the bigger noise of infrared image. The algorithm based on multi-degree and-orientation gradient fusion is presented to recognize the dot-obiects in the low-contrast infrared image polluted by heavy noise and stray waves. Experiments for detecting dim dot-targets with the above method are carried out. The result indicates that the detection performance of the multi-degree and-orientation gradient fusion is better than that of the adaptive filter and medium filter. The algorithm is especially suited to the surveillance systems of low frame rate and can detect the dot-targets with signal to clutter ratio equal to 1.